Ieee projects-2014-bulk-ieee-projects-2015-title-list-for-me-be-mphil-final-year-students

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TECHNOLOGY: JAVA
DOMAIN: CLOUD COMPUTING
S. No. IEEE TITLE ABSTRACT IEEE YEAR
1. Decentralized
Access Control
with Anonymous
Authentication of
Data Stored in
Clouds
We propose a new decentralized
access control scheme for secure data storage
in clouds that supports anonymous
authentication. In the proposed scheme, the
cloud verifies the authenticity of the series
without knowing the user’s identity before
storing data. Our scheme also has the added
feature of access control in which only valid
users are able to decrypt the stored
information. The scheme prevents replay
attacks and supports creation, modification,
and reading data stored in the cloud. We also
address user revocation. Moreover, our
authentication and access control scheme is
decentralized and robust, unlike other access
control schemes designed for clouds which
are centralized. The communication,
computation, and storage overheads are
comparable to centralized approaches.
2014

2. Modeling of
Distributed File
Systems for
Practical
Performance
Analysis
Abstract—Cloud computing has received
significant attention recently. Delivering
quality guaranteed services in clouds is highly
desired. Distributed file systems (DFSs) are
the key component of any cloud-scale data
processing middleware. Evaluating the
performance of DFSs is accordingly very
important. To avoid cost for late life cycle
performance fixes and architectural redesign,
providing performance analysis before the
deployment of DFSs is also particularly
important. In this paper, we propose a
systematic and practical performance analysis
framework, driven by architecture and design
models for defining the structure and behavior
of typical master/slave DFSs. We put forward
a configuration guideline for specifications of
configuration alternatives of such DFSs, and a
practical approach for both qualitatively and
quantitatively performance analysis of DFSs
with various configuration settings in a
systematic way. What distinguish our
approach from others is that 1) most of
2014

existing works rely on performance
measurements under a variety of
workloads/strategies, comparing with other
DFSs or running application programs, but
our approach is based on architecture and
design level models and systematically
derived performance models; 2) our approach
is able to both qualitatively and quantitatively
evaluate the performance of DFSs; and 3) our
approach not only can evaluate the overall
performance of a DFS but also its components
and individual steps. We demonstrate the
effectiveness of our approach by evaluating
Hadoop distributed file system (HDFS). A
series of real-world experiments on EC2
(Amazon Elastic Compute Cloud), Tansuo
and Inspur Clusters, were conducted to
qualitatively evaluate the effectiveness of our
approach. We also performed a set of
experiments of HDFS on EC2 to
quantitatively analyze the performance and
limitation of the metadata server of DFSs.
Results show that our approach can achieve

sufficient performance analysis. Similarly, the
proposed approach could be also applied to
evaluate other DFSs such as MooseFS, GFS,
and zFS.
3. Balancing
Performance,
Accuracy, and
Precision for
Secure Cloud
Transactions
Abstract—In distributed transactional
database systems deployed over cloud
servers, entities cooperate to form proofs of
authorizations that are justified by collections
of certified credentials. These proofs and
credentials may be evaluated and collected
over extended time periods under the risk of
having the underlying authorization policies
or the user credentials being in inconsistent
states. It therefore becomes possible for
policy-based authorization systems to make
unsafe decisions that might threaten sensitive
resources. In this paper, we highlight the
criticality of the problem. We then define the
notion of trusted transactions when dealing
with proofs of authorization. Accordingly, we
propose several increasingly stringent levels
of policy consistency constraints, and present
different enforcement approaches to
2014

guarantee the trustworthiness of transactions
executing on cloud servers. We propose a
Two-Phase Validation Commit protocol as a
solution, which is a modified version of the
basic Two-Phase Validation Commit
protocols. We finally analyze the different
approaches presented using both analytical
evaluation of the overheads and simulations
to guide the decision makers to which
approach to use.
4. A Scalable Two-
Phase Top-Down
Specialization
Approach for
Data
Anonymization
Using
MapReduce on
Cloud
Abstract—A large number of cloud services
require users to share private data like
electronic health records for data analysis or
mining, bringing privacy concerns.
Anonymizing data sets via generalization to
satisfy certain privacy requirements such as
kanonymity is a widely used category of
privacy preserving techniques. At present, the
scale of data in many cloud applications
increases tremendously in accordance with
the Big Data trend, thereby making it a
challenge for commonly used software tools
to capture, manage, and process such large-
2014

scale data within a tolerable elapsed time. As
a result, it is a challenge for existing
anonymization approaches to achieve privacy
preservation on privacy-sensitive large-scale
data sets due to their insufficiency of
scalability. In this paper, we propose a
scalable two-phase top-down specialization
(TDS) approach to anonymize large-scale
data sets using the MapReduce framework on
cloud. In both phases of our approach, we
deliberately design a group of innovative
MapReduce jobs to concretely accomplish the
specialization computation in a highly
scalable way. Experimental evaluation results
demonstrate that with our approach, the
scalability and efficiency of TDS can be
significantly improved over existing
approaches.
5. Dynamic
Optimization of
Multiattribute
Resource
Allocation in
By leveraging virtual machine (VM)
technology which provides performance and
fault isolation, cloud resources can be
provisioned on demand in a fine grained,
multiplexed manner rather than in monolithic
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Self-Organizing
Clouds
pieces. By integrating volunteer computing
into cloud architectures, we envision a
gigantic self-organizing cloud (SOC) being
formed to reap the huge potential of untapped
commodity computing power over the
Internet. Toward this new architecture where
each participant may autonomously act as
both resource consumer and provider, we
propose a fully distributed, VM-multiplexing
resource allocation scheme to manage
decentralized resources. Our approach not
only achieves maximized resource utilization
using the proportional share model (PSM),
but also delivers provably and adaptively
optimal execution efficiency. We also design
a novel multi attribute range query protocol
for locating qualified nodes. Contrary to
existing solutions which often generate bulky
messages per request, our protocol produces
only one lightweight query message per task
on the Content Addressable Network (CAN).
It works effectively to find for each task its
qualified resources under a randomized policy

that mitigates the contention among
requesters. We show the SOC with our
optimized algorithms can make an
improvement by 15-60 percent in system
throughput than a P2P Grid model. Our
solution also exhibits fairly high adaptability
in a dynamic node-churning environment.
6. Scalable and
Secure Sharing of
Personal Health
Records in Cloud
Computing Using
Attribute-Based
Encryption
Personal health record (PHR) is an emerging
patient-centric model of health information
exchange, which is often outsourced to be
stored at a third party, such as cloud
providers. However, there have been wide
privacy concerns as personal health
information could be exposed to those third
party servers and to Un authorized parties. To
assure the patients' control over access to their
own PHRs, it is a promising method to
encrypt the PHRs before outsourcing. Yet,
issues such as risks of privacy exposure,
scalability in key management, flexible
access, and efficient user revocation, have
remained the most important challenges
toward achieving fine-grained,
2013

cryptographically enforced data access
control. In this paper, we propose a novel
patient-centric framework and a suite of
mechanisms for data access control to PHRs
stored in semitrusted servers. To achieve fine-
grained and scalable data access control for
PHRs, we leverage attribute-based encryption
(ABE) techniques to encrypt each patient's
PHR file. Different from previous works in
secure data outsourcing, we focus on the
multiple data owner scenario, and divide the
users in the PHR system into multiple security
domains that greatly reduces the key
management complexity for owners and
users. A high degree of patient privacy is
guaranteed simultaneously by exploiting
multiauthority ABE. Our scheme also enables
dynamic modification of access policies or
file attributes, supports efficient on-demand
user/attribute revocation and break-glass
access under emergency scenarios. Extensive
analytical and experimental results are
presented which show the security,

scalability, and efficiency of our proposed
scheme.
7. On Data Staging
Algorithms for
Shared Data
Accesses in
Clouds
In this paper, we study the strategies for
efficiently achieving data staging and caching
on a set of vantage sites in a cloud system
with a minimum cost. Unlike the traditional
research, we do not intend to identify the
access patterns to facilitate the future
requests. Instead, with such a kind of
information presumably known in advance,
our goal is to efficiently stage the shared data
items to predetermined sites at advocated time
instants to align with the patterns while
minimizing the monetary costs for caching
and transmitting the
requested data items. To this end, we follow
the cost and network models in [1] and extend
the analysis to multiple data items, each with
single or multiple copies. Our results show
that under homogeneous cost model, when the
ratio of transmission cost and caching cost is
low, a single copy of each data item can
efficiently serve all the user requests. While
2013

in multicopy situation, we also consider the
tradeoff between the transmission cost and
caching cost by controlling the upper bounds
of transmissions and copies. The upper bound
can be given either on per-item basis or on
all-item basis. We present efficient optimal
solutions based on dynamic programming
techniques to all these cases provided that the
upper bound is polynomially bounded by the
number of service requests and the number of
distinct data items. In addition to the
homogeneous cost model, we also briefly
discuss this problem under a heterogeneous
cost model with some simple yet practical
restrictions and present a 2-approximation
algorithm to the general case. We validate our
findings by implementing a data staging
solver, whereby conducting extensive
simulation studies on the behaviors of the
algorithms.

TECHNOLOGY: JAVA
DOMAIN: Data Mining
1. Facilitating
Document
Annotation Using
Content and
Querying Value
A large number of organizations today generate
and share textual descriptions of their products,
services, and actions. Such collections of
textual data contain significant amount of
structured information, which remains buried in
the unstructured text. While information
extraction algorithms facilitate the extraction of
structured relations, they are often expensive
and inaccurate, especially when operating on
top of text that does not contain any instances
of the targeted structured information. We
present a novel alternative approach that
facilitates the generation of the structured
metadata by identifying documents that are
likely to contain information of interest and this
information is going to be subsequently useful
for querying the database. Our approach relies
on the idea that humans are more likely to add
the necessary metadata during creation time, if
prompted by the interface; or that it is much
easier for humans (and/or algorithms) to
identify the metadata when such information
actually exists in the document, instead of
naively prompting users to fill in forms with
information that is not available in the
document. As a major contribution of this
2014

paper, we present algorithms that identify
structured attributes that are likely to appear
within the document, by jointly utilizing the
content of the text and the query workload. Our
experimental evaluation shows that our
approach generates superior results compared
to approaches that rely only on the textual
content or only on the query workload, to
identify attributes of interest.
2. An Empirical
Performance
Evaluation of
Relational
Keyword Search
Techniques
Extending the keyword search paradigm to
relational data has been an active area of
research within the database and IR community
during the past decade. Many approaches have
been proposed, but despite numerous
publications, there remains a severe lack of
standardization for the evaluation of proposed
search techniques. Lack of standardization has
resulted in contradictory results from different
evaluations, and the numerous discrepancies
muddle what advantages are proffered by
different approaches. In this paper, we present
the most extensive empirical performance
evaluation of relational keyword search
techniques to appear to date in the literature.
Our results indicate that many existing search
techniques do not provide acceptable
performance for realistic retrieval tasks. In
particular, memory consumption precludes
many search techniques from scaling beyond
small data sets with tens of thousands of
vertices. We also explore the relationship
2014

between execution time and factors varied in
previous evaluations; our analysis indicates that
most of these factors have relatively little
impact on performance. In summary, our work
confirms previous claims regarding the
unacceptable performance of these search
techniques and underscores the need for
standardization in evaluations—standardization
exemplified by the IR community.
3. Set Predicates in
SQL: Enabling Set-
Level Comparisons
for Dynamically
Formed Groups
In data warehousing and OLAP applications,
scalar-level predicates in SQL become
increasingly inadequate to support a class of
operations that require set-level comparison
semantics, i.e., comparing a group of tuples
with multiple values. Currently, complex SQL
queries composed by scalar-level operations
are often formed to obtain even very simple
set-level semantics. Such queries are not only
difficult to write but also challenging for a
database engine to optimize, thus can result in
costly evaluation. This paper proposes to
augment SQL with set predicate, to bring out
otherwise obscured set-level semantics. We
studied two approaches to processing set
predicates—an aggregate function-based
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approach and a bitmap index-based approach.
Moreover, we designed a histogram-based
probabilistic method of set predicate selectivity
estimation, for optimizing queries with multiple
predicates. The experiments verified its
accuracy and effectiveness in optimizing
queries.
4. Keyword Query
Routing
Keyword search is an intuitive paradigm for
searching linked data sources on the web. We
propose to route keywords only to relevant
sources to reduce the high cost of processing
keyword search queries over all sources. We
propose a novel method for computing top-k
routing plans based on their potentials to
contain results for a given keyword query. We
employ a keyword-element relationship
summary that compactly represents
relationships between keywords and the data
elements mentioning them. A multilevel
scoring mechanism is proposed for computing
the relevance of routing plans based on scores
at the level of keywords, data elements,
element sets, and subgraphs that connect these
elements. Experiments carried out using 150
publicly available sources on the web showed
that valid plans (precision@1 of 0.92) that are
highly relevant (mean reciprocal rank of 0.89)
can be computed in 1 second on average on a
2014

single PC. Further, we show routing greatly
helps to improve the performance of keyword
search, without compromising its result quality.
5. A Rough
Hypercuboid
Approach for
Feature Selection
in Approximation
Spaces
The selection of relevant and significant
features is an important problem particularly
for data sets with large number of features. In
this regard, a new feature selection algorithm is
presented based on a rough hypercuboid
approach. It selects a set of features from a data
set by maximizing the relevance, dependency,
and significance of the selected features. By
introducing the concept of the hypercuboid
equivalence partition matrix, a novel
representation of degree of dependency of
sample categories on features is proposed to
measure the relevance, dependency, and
significance of features in approximation
spaces. The equivalence partition matrix also
offers an efficient way to calculate many more
quantitative measures to describe the
inexactness of approximate classification.
Several quantitative indices are introduced
based on the rough hypercuboid approach for
evaluating the performance of the proposed
method. The superiority of the proposed
method over other feature selection methods, in
terms of computational complexity and
classification accuracy, is established
extensively on various real-life data sets of
different sizes and dimensions.
2014

6. Active Learning of
Constraints for
Semi-Supervised
Clustering
Semi-supervised clustering aims to
improve clustering performance by considering
user supervision in the form of pairwise
constraints. In this paper, we study the active
learning problem of selecting pairwise must-
link and cannot-link constraints for
semisupervised clustering. We consider active
learning in an iterative manner where in each
iteration queries are selected based on the
current clustering solution and the existing
constraint set. We apply a general framework
that builds on the concept of neighborhood,
where neighborhoods contain “labeled
examples” of different clusters according to the
pairwise constraints. Our active learning
method expands the neighborhoods by
selecting informative points and querying their
relationship with the neighborhoods. Under this
framework, we build on the classic uncertainty-
based principle and present a novel approach
for computing the uncertainty associated with
each data point. We further introduce a
selection criterion that trades off the amount of
uncertainty of each data point with the
expected number of queries (the cost) required
to resolve this uncertainty. This allows us to
select queries that have the highest information
rate. We evaluate the proposed method on the
benchmark data sets and the results
demonstrate consistent and substantial
improvements over the current state of the art.
2014

7. Supporting Privacy
Protection in
Personalized Web
Search
Abstract—Personalized web search (PWS) has
demonstrated its effectiveness in improving the
quality of various search services on the
Internet. However, evidences show that users’
reluctance to disclose their private information
during search has become a major barrier for
the wide proliferation of PWS. We study
privacy protection in PWS applications that
model user preferences as hierarchical user
profiles. We propose a PWS framework called
UPS that can adaptively generalize profiles by
queries while respecting user specified privacy
requirements. Our runtime generalization aims
at striking a balance between two predictive
metrics that evaluate the utility of
personalization and the privacy risk of
exposing the generalized profile. We present
two greedy algorithms, namely GreedyDP and
GreedyIL, for runtime generalization. We also
provide an online prediction mechanism for
deciding whether personalizing a query is
beneficial. Extensive experiments demonstrate
the effectiveness of our framework. The
experimental results also reveal that GreedyIL
significantly outperforms GreedyDP in terms
of efficiency.
2014
8. Privacy-Preserving
Enhanced
Collaborative
Abstract—Collaborative tagging is one of the
most popular services available online, and it
allows end user to loosely classify either online
or offline resources based on their feedback,
expressed in the form of free-text labels (i.e.,
2014

Tagging tags). Although tags may not be per se sensitive
information, the wide use of collaborative
tagging services increases the risk of cross
referencing, thereby seriously compromising
user privacy. In this paper, we make a first
contribution toward the development of a
privacy-preserving collaborative tagging
service, by showing how a specific privacy-
enhancing technology, namely tag suppression,
can be used to protect end-user privacy.
Moreover, we analyze how our approach can
affect the effectiveness of a policy-based
collaborative tagging system that supports
enhanced web access functionalities, like
content filtering and discovery, based on
preferences specified by end users.
9. Event
Characterization
and Prediction
Based on Temporal
Patterns in
Dynamic Data
System
Abstract—The new method proposed in this
paper applies a multivariate reconstructed
phase space (MRPS) for identifying
multivariate temporal patterns that are
characteristic and predictive of anomalies or
events in a dynamic data system. The new
method extends the original univariate
reconstructed phase space framework, which is
based on fuzzy unsupervised clustering
method, by incorporating a new mechanism of
data categorization based on the definition of
events. In addition to modeling temporal
dynamics in a multivariate phase space, a
Bayesian approach is applied to model the first-
order Markov behavior in the multidimensional
2014

data sequences. The method utilizes an
exponential loss objective function to optimize
a hybrid classifier which consists of a radial
basis kernel function and a log-odds ratio
component. We performed experimental
evaluation on three data sets to demonstrate the
feasibility and effectiveness of the proposed
approach.
10. Discovering
Emerging Topics in
Social Streams via
Link-Anomaly
Detection
Abstract—Detection of emerging topics is now
receiving renewed interest motivated by the
rapid growth of social networks. Conventional-
term-frequency-based approaches may not be
appropriate in this context, because the
information exchanged in social network posts
include not only text but also images, URLs,
and videos. We focus on emergence of topics
signaled by social aspects of theses networks.
Specifically, we focus on mentions of users—
links between users that are generated
dynamically (intentionally or unintentionally)
through replies, mentions, and retweets. We
propose a probability model of the mentioning
behavior of a social network user, and propose
to detect the emergence of a new topic from the
anomalies measured through the model.
Aggregating anomaly scores from hundreds of
users, we show that we can detect emerging
topics only based on the reply/mention
relationships in social-network posts. We
demonstrate our technique in several real data
sets we gathered from Twitter. The
2014

experiments show that the proposed mention-
anomaly-based approaches can detect new
topics at least as early as text-anomaly-based
approaches, and in some cases much earlier
when the topic is poorly identified by the
textual contents in posts.
11. A New Algorithm
for Inferring User
Search Goals with
Feedback Sessions
For a broad-topic and ambiguous query,
different users may have different search goals
when they submit it to a search engine. The
inference and analysis of user search goals can
be very useful in improving search engine
relevance and user experience. In this paper,
we propose a novel approach to infer user
search goals by analyzing search engine query
logs. First, we propose a framework to discover
different user search goals for a query by
clustering the proposed feedback sessions.
Feedback sessions are constructed from user
click-through logs and can efficiently reflect
the information needs of users. Second, we
propose a novel approach to generate pseudo-
documents to better represent the feedback
sessions for clustering. Finally, we propose a
new criterion )“Classified Average Precision
2013

(CAP)” to evaluate the performance of
inferring user search goals. Experimental
results are presented using user click-through
logs from a commercial search engine to
validate the effectiveness of our proposed
methods.
12. Facilitating
Effective User
Navigation through
Website Structure
Improvement
Designing well-structured websites to facilitate
effective user navigation has long been a
challenge. A primary reason is that the web
developers' understanding of how a website
should be structured can be considerably
different from that of the users. While various
methods have been proposed to relink
webpages to improve navigability using user
navigation data, the completely reorganized
new structure can be highly unpredictable, and
the cost of disorienting users after the changes
remains unanalyzed. This paper addresses how
to improve a website without introducing
substantial changes. Specifically, we propose a
mathematical programming model to improve
the user navigation on a website while
minimizing alterations to its current structure.
2013

Results from extensive tests conducted on a
publicly available real data set indicate that our
model not only significantly improves the user
navigation with very few changes, but also can
be effectively solved. We have also tested the
model on large synthetic data sets to
demonstrate that it scales up very well. In
addition, we define two evaluation metrics and
use them to assess the performance of the
improved website using the real data set.
Evaluation results confirm that the user
navigation on the improved structure is indeed
greatly enhanced. More interestingly, we find
that heavily disoriented users are more likely to
benefit from the improved structure than the
less disoriented users.
13. Building a Scalable
Database-Driven
Reverse Dictionary
In this paper, we describe the design and
implementation of a reverse dictionary. Unlike
a traditional forward dictionary, which maps
from words to their definitions, a reverse
dictionary takes a user input phrase describing
the desired concept, and returns a set of
candidate words that satisfy the input phrase.
2013

This work has significant application not only
for the general public, particularly those who
work closely with words, but also in the
general field of conceptual search. We present
a set of algorithms and the results of a set of
experiments showing the retrieval accuracy of
our methods and the runtime response time
performance of our implementation. Our
experimental results show that our approach
can provide significant improvements in
performance scale without sacrificing the
quality of the result. Our experiments
comparing the quality of our approach to that
of currently available reverse dictionaries show
that of our approach can provide significantly
higher quality over either of the other currently
available implementation
TECHNOLOGY: JAVA
DOMAIN: DEPENDABLE & SECURE COMPUTING
1. Secure Two-Party Privacy-preserving data publishing addresses 2014

Differentially
Private Data
Release for
Vertically
Partitioned Data
the problem of disclosing sensitive data when
mining for useful information. Among the
existing privacy models, _-differential privacy
provides one of the strongest privacy
guarantees. In this paper, we address the
problem of private data publishing, where
different attributes for the same set of
individuals are held by two parties. In
particular, we present an algorithm for
differentially private data release for vertically
partitioned data between two parties in the semi
honest adversary model. To achieve this, we
first present a two-party protocol for the
exponential mechanism. This protocol can be
used as a sub protocol by any other algorithm
that requires the exponential mechanism in a
distributed setting. Furthermore, we propose a
two party algorithm that releases differentially
private data in a secure way according to the
definition of secure multiparty computation.
Experimental results on real-life data suggest
that the proposed algorithm can effectively
preserve information for a data mining task.

2. Bandwidth
Distributed Denial
of Service: Attacks
and Defenses
The Internet is vulnerable to bandwidth
distributed denial-of-service (BW-DDoS)
attacks, wherein many hosts send a huge
number of packets to cause congestion and
disrupt legitimate traffic. So far, BW-DDoS
attacks have employed relatively crude,
inefficient, brute-force mechanisms; future
attacks might be significantly more effective
and harmful. To meet the increasing threats,
more advanced defenses are necessary.
2014
3. k-Zero Day Safety:
A Network
Security Metric for
Measuring the Risk
of Unknown
Vulnerabilities
By enabling a direct comparison of different
security solutions with respect to their relative
effectiveness, a network security metric may
provide quantifiable evidences to assist security
practitioners in securing computer networks.
However, research on security metrics has been
hindered by difficulties in handling zero-day
attacks exploiting unknown vulnerabilities. In
fact, the security risk of unknown
vulnerabilities has been considered as
something un-measurable due to the less
predictable nature of software flaws. This
causes a major difficulty to security metrics,
2014

because a more secure configuration would be
of little value if it were equally susceptible to
zero-day attacks. In this paper, we propose a
novel security metric, k-zero day safety, to
address this issue. Instead of attempting to rank
unknown vulnerabilities, our metric counts how
many such vulnerabilities would be required
for compromising network assets; a larger
count implies more security because the
likelihood of having more unknown
vulnerabilities available, applicable, and
exploitable all at the same time will be
significantly lower. We formally define the
metric, analyze the complexity of computing
the metric, devise heuristic algorithms for
intractable cases, and finally demonstrate
through case studies that applying the metric to
existing network security practices may
generate actionable knowledge.
4. Security Games for
Node Localization
through Verifiable
Multilateration
Most applications of wireless sensor
networks (WSNs) rely on data about the
positions of sensor nodes, which are not
necessarily known beforehand. Several
2014

localization approaches have been proposed but
most of them omit to consider that WSNs could
be deployed in adversarial settings, where
hostile nodes under the control of an attacker
coexist with faithful ones. Verifiable
multilateration (VM) was proposed to cope
with this problem by leveraging on a set of
trusted landmark nodes that act as verifiers.
Although VM is able to recognize reliable
localization measures, it allows for regions of
undecided positions that can amount to the 40
percent of the monitored area. We studied the
properties of VM as a non cooperative two-
player game where the first player employs a
number of verifiers to do VM computations
and the second player controls a malicious
node. The verifiers aim at securely localizing
malicious nodes, while malicious nodes strive
to masquerade as unknown and to pretend false
positions. Thanks to game theory, the
potentialities of VM are analyzed with the aim
of improving the defender’s strategy. We found
that the best placement for verifiers is an

equilateral triangle with edge equal to the
power range R, and maximum deception in the
undecided region is approximately 0:27R.
Moreover, we characterized—in terms of the
probability of choosing an unknown node to
examine further—the strategies of the players.
5. On Inference-Proof
View Processing of
XML Documents
This work aims at treating the inference
problem in XML documents that are assumed
to represent potentially incomplete information.
The inference problem consists in providing a
control mechanism for enforcing inference-
usability confinement of XML documents.
More formally, an inference-proof view of an
XML document is required to be both
indistinguishable from the actual XML
document to the clients under their inference
capabilities, and to neither contain nor imply
any confidential information. We present an
algorithm for generating an inference-proof
view by weakening the actual XML document,
i.e., eliminating confidential information and
other information that could be used to infer
confidential information. In order to avoid
2013

inferences based on the schema of the XML
documents, the DTD of the actual XML
document is modified according to the
weakening operations as well, such that the
modified DTD conforms with the generated
inference-proof view.
6. SORT:A Self-
Organizing Trust
Model for Peer-to-
Peer Systems
2013
TECHNOLOGY: JAVA
DOMAIN: IMAGE PROCESSING
1. Large
Discriminative
Structured Set
Prediction
Modeling With
Max-Margin
Markov Network
for Lossless Image
Abstract—Inherent statistical correlation for
context-based prediction and structural
interdependencies for local coherence is not
fully exploited in existing lossless image
coding schemes. This paper proposes a novel
prediction model where the optimal correlated
prediction for a set of pixels is obtained in the
sense of the least code length. It not only
2014

Coding exploits the spatial statistical correlations for
the optimal prediction directly based on 2D
contexts, but also formulates the data-driven
structural interdependencies to make the
prediction error coherent with the underlying
probability distribution for coding. Under the
joint constraints for local coherence, max-
margin Markov networks are incorporated to
combine support vector machines structurally
to make maxmargin estimation for a correlated
region. Specifically, it aims to produce multiple
predictions in the blocks with the model
parameters learned in such a way that the
distinction between the actual pixel and all
possible estimations is maximized. It is proved
that, with the growth of sample size, the
prediction error is asymptotically upper
bounded by the training error under the
decomposable loss function. Incorporated into
the lossless image coding framework, the
proposed model outperforms most prediction
schemes reported.
2. Multi-Illuminant Abstract—Most existing color constancy 2014

Estimation With
Conditional
Random Fields
algorithms assume uniform illumination.
However, in real-world scenes, this is not often
the case. Thus, we propose a novel framework
for estimating the colors of multiple illuminants
and their spatial distribution in the scene. We
formulate this problem as an energy
minimization task within a conditional random
field over a set of local illuminant estimates. In
order to quantitatively evaluate the proposed
method, we created a novel data set of two-
dominant illuminant images comprised of
laboratory, indoor, and outdoor scenes. Unlike
prior work, our database includes accurate
pixel wise ground truth illuminant information.
The performance of our method is evaluated on
multiple data sets. Experimental results show
that our framework clearly outperforms single
illuminant estimators as well as a recently
proposed multi illuminant estimation approach.
3. Saliency-Aware
Video
Compression
Abstract—In region-of-interest (ROI)-
based video coding, ROI parts of the frame are
encoded with higher quality than non-ROI
parts. At low bit rates, such encoding may
2014

produce attention grabbing coding artifacts,
which may draw viewer’s attention away from
ROI, thereby degrading visual quality. In this
paper, we present a saliency-aware video
compression method for ROI-based video
coding. The proposed method aims at reducing
salient coding artifacts in non-ROI parts of the
frame in order to keep user’s attention on ROI.
Further, the method allows saliency to increase
in high quality parts of the frame, and allows
saliency to reduce in non-ROI parts.
Experimental results indicate that the proposed
method is able to improve visual quality of
encoded video relative to conventional rate
distortion optimized video coding, as well as
two state-of-the art perceptual video coding
methods.
4. Translation
Invariant
Directional
Framelet
Transform
Combined With
Abstract—This paper is devoted to the
study of a directional lifting transform for
wavelet frames. A non sub-sampled lifting
structure is developed to maintain the
translation invariance as it is an important
property in image denoising. Then, the
2014

Gabor Filters for
Image Denoising
directionality of the lifting-based tight frame is
explicitly discussed, followed by a specific
translation invariant directional framelet
transform (TIDFT). The TIDFT has two
framelets ψ1,ψ2 with vanishing moments of
order two and one respectively, which are able
to detect singularities in a given direction set. It
provides an efficient and sparse representation
for images containing rich textures along with
properties of fast implementation and perfect
reconstruction. In addition, an adaptive block-
wise orientation estimation method based on
Gabor filters is presented instead of the
conventional minimization of residuals.
Furthermore, the TIDFT is utilized to exploit
the capability of image denoising,
incorporating the MAP estimator for
multivariate exponential distribution.
Consequently, the TIDFT is able to eliminate
the noise effectively while preserving the
textures simultaneously. Experimental results
show that the TIDFT outperforms some other
frame-based denoising methods, such as

contourlet and shearlet, and is competitive to
the state-of-the-art denoising approaches.
5. Vector-Valued
Image Processing
by Parallel Level
Sets
Vector-valued images such as RGB
color images or multimodal medical images
show a strong inter channel correlation, which
is not exploited by most image processing
tools. We propose a new notion of treating
vector-valued images which is based on the
angle between the spatial gradients of their
channels. Through minimizing a cost functional
that penalizes large angles, images with parallel
level sets can be obtained. After formally
introducing this idea and the corresponding
cost functionals, we discuss their Gâteaux
derivatives that lead to a diffusion-like gradient
descent scheme. We illustrate the properties of
this cost functional by several examples in
denoising and demo saicking of RGB color
images. They show that parallel level sets are a
suitable concept for color image enhancement.
Demosaicking with parallel level sets gives
visually perfect results for low noise levels.
Furthermore, the proposed functional yields
2014

sharper images than the other approaches in
comparison.
6. Circular Re ranking
for Visual Search
Search re ranking is regarded as a common way
to boost retrieval precision. The problem
nevertheless is not trivial especially when there
are multiple features or modalities to be
considered for search, which often happens in
image and video retrieval. This paper proposes
a new reranking algorithm, named circular
reranking, that reinforces the mutual exchange
of information across multiple modalities for
improving search performance, following the
philosophy that strong performing modality
could learn from weaker ones, while weak
modality does benefit from interacting with
stronger ones. Technically, circular reranking
conducts multiple runs of random walks
through exchanging the ranking scores among
different features in a cyclic manner. Unlike the
existing techniques, the reranking procedure
encourages interaction among modalities to
seek a consensus that are useful for reranking.
In this paper, we study several properties of
2013

circular reranking, including how and which
order of information propagation should be
configured to fully exploit the potential of
modalities for reranking. Encouraging results
are reported for both image and video retrieval
on Microsoft Research Asia Multimedia image
dataset and TREC Video Retrieval Evaluation
2007-2008 datasets, respectively.
7. Efficient Method
for Content Re
construction With
Self-Embedding
This paper presents a new model of the content
reconstruction problem in self-embedding
systems, based on an erasure communication
channel. We explain why such a model is a
good fit for this problem, and how it can be
practically implemented with the use of digital
fountain codes. The proposed method is based
on an alternative approach to spreading the
reference information over the whole image,
which has recently been shown to be of critical
importance in the application at hand. Our
paper presents a theoretical analysis of the
inherent restoration trade-offs. We analytically
derive formulas for the reconstruction success
bounds, and validate them experimentally with
2013

Monte Carlo simulations and a reference image
authentication system. We perform an
exhaustive reconstruction quality assessment,
where the presented reference scheme is
compared to five state-of-the-art alternatives in
a common evaluation scenario. Our paper leads
to important insights on how self-embedding
schemes should be constructed to achieve
optimal performance. The reference
authentication system designed according to
the presented principles allows for high-quality
reconstruction, regardless of the amount of the
tampered content. The average reconstruction
quality, measured on 10000 natural images is
37 dB, and is achievable even when 50% of the
image area becomes tampered.
8. Modeling Iris Code
and Its Variants as
Convex Polyhedral
Cones and Its
Security
Implications
Iris Code, developed by Daugman, in 1993, is
the most influential iris recognition algorithm.
A thorough understanding of Iris Code is
essential, because over 100 million persons
have been enrolled by this algorithm and many
biometric personal identification and template
protection methods have been developed based
2013

on Iris Code. This paper indicates that a
template produced by Iris Code or its variants
is a convex polyhedral cone in a hyperspace. Its
central ray, being a rough representation of the
original biometric signal, can be computed by a
simple algorithm, which can often be
implemented in one Matlab command line. The
central ray is an expected ray and also an
optimal ray of an objective function on a group
of distributions. This algorithm is derived from
geometric properties of a convex polyhedral
cone but does not rely on any prior knowledge
(e.g., iris images). The experimental results
show that biometric templates, including iris
and palmprint templates, produced by different
recognition methods can be matched through
the central rays in their convex polyhedral
cones and that templates protected by a method
extended from IrisCode can be broken into.
These experimental results indicate that,
without a thorough security analysis, convex
polyhedral cone templates cannot be assumed
secure. Additionally, the simplicity of the

algorithm implies that even junior hackers
without knowledge of advanced image
processing and biometric databases can still
break into protected templates and reveal
relationships among templates produced by
different recognition methods.
9. Robust Document
Image Binarization
Technique for
Degraded
Document Images
Segmentation of text from badly degraded
document images is a very challenging task due
to the high inter/intra-variation between the
document background and the foreground text
of different document images. In this paper, we
propose a novel document image binarization
technique that addresses these issues by using
adaptive image contrast. The adaptive image
contrast is a combination of the local image
contrast and the local image gradient that is
tolerant to text and background variation
caused by different types of document
degradations. In the proposed technique, an
adaptive contrast map is first constructed for an
input degraded document image. The contrast
map is then binarized and combined with
Canny's edge map to identify the text stroke
2013

edge pixels. The document text is further
segmented by a local threshold that is estimated
based on the intensities of detected text stroke
edge pixels within a local window. The
proposed method is simple, robust, and
involves minimum parameter tuning. It has
been tested on three public datasets that are
used in the recent document image binarization
contest (DIBCO) 2009 & 2011 and handwritten
-DIBCO 2010 and achieves accuracies of
93.5%, 87.8%, and 92.03%, respectively, that
are significantly higher than or close to that of
the best-performing methods reported in the
three contests. Experiments on the Bickley
diary dataset that consists of several
challenging bad quality document images also
show the superior performance of our proposed
method, compared with other techniques.
10. Per-Colorant-
Channel Color
Barcodes for
Mobile
Applications: An
We propose a color barcode framework for
mobile phone applications by exploiting the
spectral diversity afforded by the cyan (C),
magenta (M), and yellow (Y) print colorant
channels commonly used for color printing and
2013

Interference
Cancellation
Framework
the complementary red (R), green (G), and blue
(B) channels, respectively, used for capturing
color images. Specifically, we exploit this
spectral diversity to realize a three-fold
increase in the data rate by encoding
independent data in the C, M, and Y print
colorant channels and decoding the data from
the complementary R, G, and B channels
captured via a mobile phone camera. To
mitigate the effect of cross-channel interference
among the print-colorant and capture color
channels, we develop an algorithm for
interference cancellation based on a physically
-motivated mathematical model for the print
and capture processes. To estimate the model
parameters required for cross-channel
interference cancellation, we propose two
alternative methodologies: a pilot block
approach that uses suitable selections of colors
for the synchronization blocks and an
expectation maximization approach that
estimates the parameters from regions encoding
the data itself. We evaluate the performance of

the proposed framework using specific
implementations of the framework for two of
the most commonly used barcodes in mobile
applications, QR and Aztec codes.
Experimental results show that the proposed
framework successfully overcomes the impact
of the color interference, providing a low bit
error rate and a high decoding rate for each of
the colorant channels when used with a
corresponding error correction scheme.
TECHNOLOGY: JAVA
DOMAIN: MOBILE COMPUTING
1. Cooperative
Spectrum Sharing:
A Contract-Based
Approach
Abstract—Providing economic incentives to all
parties involved is essential for the success of
dynamic spectrum access. Cooperative
spectrum sharing is one effective way to
achieve this, where secondary users (SUs) relay
traffics for primary users (PUs) in exchange for
dedicated spectrum access time for SUs’ own
communications. In this paper, we study the
2014

cooperative spectrum sharing under incomplete
information, where SUs’ wireless
characteristics are private information and not
known by a PU. We model the PU-SU
interaction as a labor market using contract
theory. In contract theory, the employer
generally does not completely know
employees’ private information before the
employment and needs to offers employees a
contract under incomplete information. In our
problem, the PU and SUs are, respectively, the
employer and employees, and the contract
consists of a set of items representing
combinations of spectrum accessing time (i.e.,
reward) and relaying power (i.e., contribution).
We study the optimal contract design for both
weakly and strongly incomplete information
scenarios. In the weakly incomplete
information scenario, we show that the PU will
optimally hire the most efficient SUs and the
PU achieves the same maximum utility as in
the complete information benchmark. In the
strongly incomplete information scenario,

however, the PU may conservatively hire less
efficient SUs as well. We further propose a
decompose-and-compare (DC) approximate
algorithm that achieves a close-to-optimal
contract. We further show that the PU’s
average utility loss due to the suboptimal DC
algorithm and the strongly incomplete
information are relatively small (less than 2 and
1.3 percent, respectively, in our numerical
results with two SU types).
2. Energy-Aware
Resource
Allocation
Strategies for LTE
Uplink with
Synchronous
HARQ Constraints
Abstract—In this paper, we propose a
framework for energy efficient resource
allocation in multiuser localized SC-FDMA
with synchronous HARQ constraints. Resource
allocation is formulated as a two-stage problem
where resources are allocated in both time and
frequency. The impact of retransmissions on
the time-frequency problem segmentation is
handled through the use of a novel block
scheduling interval specifically designed for
synchronous HARQ to ensure uplink users do
not experience ARQ blocking. Using this
framework, we formulate the optimal margin
2014

adaptive allocation problem, and based on its
structure, we propose two suboptimal
approaches to minimize average power
allocation required for resource allocation
while attempting to reduce complexity. Results
are presented for computational complexity and
average power allocation relative to system
complexity and data rate, and comparisons are
made between the proposed optimal and
suboptimal approaches.
3. Preserving
Location Privacy in
Geosocial
Applications
Abstract—Using geosocial applications,
such as FourSquare, millions of people interact
with their surroundings through their friends
and their recommendations. Without adequate
privacy protection, however, these systems can
be easily misused, for example, to track users
or target them for home invasion. In this paper,
we introduce LocX, a novel alternative that
provides significantly improved location
privacy without adding uncertainty into query
results or relying on strong assumptions about
server security. Our key insight is to apply
secure user-specific, distance-preserving
2014

coordinate transformations to all location data
shared with the server. The friends of a user
share this user’s secrets so they can apply the
same transformation. This allows all location
queries to be evaluated correctly by the server,
but our privacy mechanisms guarantee that
servers are unable to see or infer the actual
location data from the transformed data or from
the data access. We show that LocX provides
privacy even against a powerful adversary
model, and we use prototype measurements to
show that it provides privacy with very little
performance overhead, making it suitable for
today’s mobile devices.
4. Snapshot and
Continuous Data
Collection in
Probabilistic
Wireless Sensor
Networks
Abstract—Data collection is a common
operation of Wireless Sensor Networks
(WSNs), of which the performance can be
measured by its achievable network capacity.
Most existing works studying the network
capacity issue are based on the unpractical
model called deterministic network model. In
this paper, a more reasonable model,
probabilistic network model, is considered. For
2014

snapshot data collection, we propose a novel
Cell-based Path Scheduling (CPS) algorithm
that achieves capacity of Ω (1/5ѡ ln n. W) in
the sense of the worst case and order-optimal
capacity in the sense of expectation, where n is
the number of sensor nodes, ѡ is a constant,
and W is the data transmitting rate. For
continuous data collection, we propose a Zone-
based Pipeline Scheduling (ZPS) algorithm.
ZPS significantly speeds up the continuous data
collection process by forming a data
transmission pipeline, and achieves a capacity
gain of N
√ √⁄ ⁄ times better
than the optimal capacity of the snapshot data
collection scenario in order in the sense of the
worst case, where N is the number of snapshots
in a continuous data collection task. The
simulation results also validate that the
proposed algorithms significantly improve
network capacity compared with the existing
works.
5. A QoS-Oriented Abstract—As wireless communication 2014

Distributed
Routing Protocol
for Hybrid
Wireless Networks
gains popularity, significant research has been
devoted to supporting real-time transmission
with stringent Quality of Service (QoS)
requirements for wireless applications. At the
same time, a wireless hybrid network that
integrates a mobile wireless ad hoc network
(MANET) and a wireless infrastructure
network has been proven to be a better
alternative for the next generation wireless
networks. By directly adopting resource
reservation-based QoS routing for MANETs,
hybrids networks inherit invalid reservation
and race condition problems in MANETs. How
to guarantee the QoS in hybrid networks
remains an open problem. In this paper, we
propose a QoS-Oriented Distributed routing
protocol (QOD) to enhance the QoS support
capability of hybrid networks. Taking
advantage of fewer transmission hops and
anycast transmission features of the hybrid
networks, QOD transforms the packet routing
problem to a resource scheduling problem.
QOD incorporates five algorithms: 1) a QoS-

guaranteed neighbor selection algorithm to
meet the transmission delay requirement, 2) a
distributed packet scheduling algorithm to
further reduce transmission delay, 3) a
mobility-based segment resizing algorithm that
adaptively adjusts segment size according to
node mobility in order to reduce transmission
time, 4) a traffic redundant elimination
algorithm to increase the transmission
throughput, and 5) a data redundancy
elimination-based transmission algorithm to
eliminate the redundant data to further
improve the transmission QoS. Analytical and
simulation results based on the random way-
point model and the real human mobility model
show that QOD can provide high QoS
performance in terms of overhead, transmission
delay, mobility-resilience, and scalability.
6. Cooperative
Caching for
Efficient Data
Access in
Disruption Tolerant
Abstract—Disruption tolerant networks
(DTNs) are characterized by low node density,
unpredictable node mobility, and lack of global
network information. Most of current research
efforts in DTNs focus on data forwarding, but
2014

Networks only limited work has been done on providing
efficient data access to mobile users. In this
paper, we propose a novel approach to support
cooperative caching in DTNs, which enables
the sharing and coordination of cached data
among multiple nodes and reduces data access
delay. Our basic idea is to intentionally cache
data at a set of network central locations
(NCLs), which can be easily accessed by other
nodes in the network. We propose an efficient
scheme that ensures appropriate NCL selection
based on a probabilistic selection metric and
coordinates multiple caching nodes to optimize
the tradeoff between data accessibility and
caching overhead. Extensive trace-driven
simulations show that our approach
significantly improves data access performance
compared to existing schemes.
7. Real-Time
Misbehavior
Detection in IEEE
802.11-Based
Wireless Networks:
Abstract—The distributed nature of the
CSMA/CA-based wireless protocols, for
example, the IEEE 802.11 distributed
coordinated function (DCF), allows malicious
nodes to deliberately manipulate their backoff
2014

An Analytical
Approach
parameters and, thus, unfairly gain a large
share of the network throughput. In this paper,
we first design a real-time backoff misbehavior
detector, termed as the fair share detector (FS
detector), which exploits the nonparametric
cumulative sum (CUSUM) test to quickly find
a selfish malicious node without any a priori
knowledge of the statistics of the selfish
misbehavior. While most of the existing
schemes for selfish misbehavior detection
depend on heuristic parameter configuration
and experimental performance evaluation, we
develop a Markov chain-based analytical
model to systematically study the performance
of the FS detector in real-time backoff
misbehavior detection. Based on the analytical
model, we can quantitatively compute the
system configuration parameters for guaranteed
performance in terms of average false positive
rate, average detection delay, and missed
detection ratio under a detection delay
constraint. We present thorough simulation
results to confirm the accuracy of our

theoretical analysis as well as demonstrate the
performance of the developed FS detector.
8. A Neighbor
Coverage-Based
Probabilistic
Rebroadcast for
Reducing Routing
Overhead in
Mobile Ad Hoc
Networks
Due to high mobility of nodes in mobile ad hoc
networks (MANETs), there exist frequent link
breakages which lead to frequent path failures
and route discoveries. The overhead of a route
discovery cannot be neglected. In a route
discovery, broadcasting is a fundamental and
effective data dissemination mechanism, where
a mobile node blindly rebroadcasts the first
received route request packets unless it has a
route to the destination, and thus it causes the
broadcast storm problem. In this paper, we
propose a neighbor coverage-based
probabilistic rebroadcast protocol for reducing
routing overhead in MANETs. In order to
effectively exploit the neighbor coverage
knowledge, we propose a novel rebroadcast
delay to determine the rebroadcast order, and
then we can obtain the more accurate additional
coverage ratio by sensing neighbor coverage
knowledge. We also define a connectivity
factor to provide the node density adaptation.
2013

By combining the additional coverage ratio and
connectivity factor, we set a reasonable
rebroadcast probability. Our approach
combines the advantages of the neighbor
coverage knowledge and the probabilistic
mechanism, which can significantly decrease
the number of retransmissions so as to reduce
the routing overhead, and can also improve the
routing performance.
9. Relay Selection for
Geographical
Forwarding in
Sleep-Wake
Cycling Wireless
Sensor Networks
Our work is motivated by geographical
forwarding of sporadic alarm packets to a base
station in a wireless sensor network (WSN),
where the nodes are sleep-wake cycling
periodically and asynchronously. We seek to
develop local forwarding algorithms that can be
tuned so as to tradeoff the end-to-end delay
against a total cost, such as the hop count or
total energy. Our approach is to solve, at each
forwarding node enroute to the sink, the local
forwarding problem of minimizing one-hop
waiting delay subject to a lower bound
constraint on a suitable reward offered by the
next-hop relay; the constraint serves to tune the
2013

tradeoff. The reward metric used for the local
problem is based on the end-to-end total cost
objective (for instance, when the total cost is
hop count, we choose to use the progress
toward sink made by a relay as the reward).
The forwarding node, to begin with, is
uncertain about the number of relays, their
wake-up times, and the reward values, but
knows the probability distributions of these
quantities. At each relay wake-up instant, when
a relay reveals its reward value, the forwarding
node's problem is to forward the packet or to
wait for further relays to wake-up. In terms of
the operations research literature, our work can
be considered as a variant of the asset selling
problem. We formulate our local forwarding
problem as a partially observable Markov
decision process (POMDP) and obtain inner
and outer bounds for the optimal policy.
Motivated by the computational complexity
involved in the policies derived out of these
bounds, we formulate an alternate simplified
model, the optimal policy for which is a simple

threshold rule. We provide simulation results to
compare the performance of the inner and outer
bound policies against the simple policy, and
also against the optimal policy when the source
knows the exact number of relays. Observing
the good performance and the ease of
implementation of the simple policy, we apply
it to our motivating problem, i.e., local
geographical routing of sporadic alarm packets
in a large WSN. We compare the end-to-end
performance (i.e., average total delay and
average total cost) obtained by the simple
policy, when used for local geographical
forwarding, against that obtained by the
globally optimal forwarding algorithm
proposed by Kim et al.
10. Toward Privacy
Preserving and
Collusion
Resistance in a
Location Proof
Updating System
Today's location-sensitive service relies on
user's mobile device to determine the current
location. This allows malicious users to access
a restricted resource or provide bogus alibis by
cheating on their locations. To address this
issue, we propose A Privacy Preserving
LocAtion proof Updating System (APPLAUS)
2013

in which colocated Bluetooth enabled mobile
devices mutually generate location proofs and
send updates to a location proof server.
Periodically changed pseudonyms are used by
the mobile devices to protect source location
privacy from each other, and from the untrusted
location proof server. We also develop user-
centric location privacy model in which
individual users evaluate their location privacy
levels and decide whether and when to accept
the location proof requests. In order to defend
against colluding attacks, we also present
betweenness ranking-based and correlation
clustering-based approaches for outlier
detection. APPLAUS can be implemented with
existing network infrastructure, and can be
easily deployed in Bluetooth enabled mobile
devices with little computation or power cost.
Extensive experimental results show that
APPLA US can effectively provide location
proofs, significantly preserve the source
location privacy, and effectively detect
colluding attacks.

11. Distributed
Cooperation and
Diversity for
Hybrid Wireless
Networks
In this paper, we propose a new Distributed
Cooperation and Diversity Combining
framework. Our focus is on heterogeneous
networks with devices equipped with two types
of radio frequency (RF) interfaces: short-range
high-rate interface (e.g., IEEE802.11), and a
long-range low-rate interface (e.g., cellular)
communicating over urban Rayleigh fading
channels. Within this framework, we propose
and evaluate a set of distributed cooperation
techniques operating at different hierarchical
levels with resource constraints such as short
-range RF bandwidth. We propose a Priority
Maximum-Ratio Combining (PMRC)
technique, and a Post Soft-Demodulation
Combining (PSDC) technique. We show that
the proposed techniques achieve significant
improvements on Signal to Noise Ratio(SNR),
Bit Error Rate (BER) and throughput through
analysis, simulation, and experimentation on
our software radio testbed. Our results also
indicate that, under several communication
scenarios, PMRC and PSDC can improve the
2013

throughput performance by over an order of
magnitude.
12. Toward a
Statistical Frame
work for Source
Anonymity in
Sensor Networks
In certain applications, the locations of events
reported by a sensor network need to remain
anonymous. That is, unauthorized observers
must be unable to detect the origin of such
events by analyzing the network traffic. Known
as the source anonymity problem, this problem
has emerged as an important topic in the
security of wireless sensor networks, with
variety of techniques based on different
adversarial assumptions being proposed. In this
work, we present a new framework for
modeling, analyzing, and evaluating anonymity
in sensor networks. The novelty of the
proposed framework is twofold: first, it
introduces the notion of interval
indistinguishability” and provides a
quantitative measure to model anonymity in
wireless sensor networks; second, it maps
source anonymity to the statistical problem of
binary hypothesis testing with nuisance
parameters. We then analyze existing solutions
2013

for designing anonymous sensor networks
using the proposed model. We show how
mapping source anonymity to binary
hypothesis testing with nuisance parameters
leads to converting the problem of exposing
private source information into searching for an
appropriate data transformation that removes or
minimize the effect of the nuisance
information. By doing so, we transform the
problem from analyzing real-valued sample
points to binary codes, which opens the door
for coding theory to be incorporated into the
study of anonymous sensor networks. Finally,
we discuss how existing solutions can be
modified to improve their anonymity.
13. Vampire Attacks:
Draining Life from
Wireless Ad Hoc
Sensor Networks
Ad hoc low-power wireless networks are an
exciting research direction in sensing and
pervasive computing. Prior security work in
this area has focused primarily on denial of
communication at the routing or medium
access control levels. This paper explores
resource depletion attacks at the routing
protocol layer, which permanently disable
2013

networks by quickly draining nodes' battery
power. These "Vampire” attacks are not
specific to any specific protocol, but rather rely
on the properties of many popular classes of
routing protocols. We find that all examined
protocols are susceptible to Vampire attacks,
which are devastating, difficult to detect, and
are easy to carry out using as few as one
malicious insider sending only protocol-
compliant messages. In the worst case, a single
Vampire can increase network-wide energy
usage by a factor of O(N), where N in the
number of network nodes. We discuss methods
to mitigate these types of attacks, including a
new proof-of-concept protocol that provably
bounds the damage caused by Vampires during
the packet forwarding phase.
TECHNOLOGY: JAVA
DOMAIN: NETWORKING
1. Fast Regular Abstract—Regular expression (RE) 2014

Expression
Matching Using
Small TCAM
matching is a core component of deep packet
inspection in modern networking and security
devices. In this paper, we propose the first
hardware-based RE matching approach that
uses ternary content addressable memory
(TCAM), which is available as off-the-shelf
chips and has been widely deployed in modern
networking devices for tasks such as packet
classification. We propose three novel
techniques to reduce TCAM space and improve
RE matching speed: transition sharing, table
consolidation, and variable striding. We tested
our techniques on eight real-world RE sets, and
our results show that small TCAMs can be used
to store large deterministic finite automata
(DFAs) and achieve potentially high RE
matching throughput. For space, we can store
each of the corresponding eight DFAs with 25
000 states in a 0.59-Mb TCAM chip. Using a
different TCAM encoding scheme that
facilitates processing multiple characters per
transition, we can achieve potential RE
matching throughput of 10–19 Gb/s for each of

the eight DFAs using only a single 2.36-Mb
TCAMchip.
2. Green Networking
With Packet
Processing
Engines: Modeling
and Optimization
Abstract—With the aim of controlling
power consumption in metro/transport and core
networks, we consider energy-aware devices
able to reduce their energy requirements by
adapting their performance. In particular, we
focus on state-of-the-art packet processing
engines, which generally represent the most
energy-consuming components of network
devices, and which are often composed of a
number of parallel pipelines to “divide and
conquer” the incoming traffic load. Our goal is
to control both the power configuration of
pipelines and the way to distribute traffic flows
among them. We propose an analytical model
to accurately represent the impact of green
network technologies (i.e., low power idle and
adaptive rate) on network- and energy-aware
performance indexes. The model has been
validated with experimental results, performed
by using energy-aware software routers loaded
by real-world traffic traces. The achieved
2014

results demonstrate how the proposed model
can effectively represent energy- and network-
aware performance indexes. On this basis, we
propose a constrained optimization policy,
which seeks the best tradeoff between power
consumption and packet latency times. The
procedure aims at dynamically adapting the
energy-aware device configuration to minimize
energy consumption while coping with
incoming traffic volumes and meeting network
performance constraints. In order to deeply
understand the impact of such policy, a number
of tests have been performed by using
experimental data from software router
architectures and real-world traffic traces.
3. On Sample-Path
Optimal Dynamic
Scheduling for
Sum-Queue
Minimization in
Forests
Abstract—We investigate the problem
of minimizing the sum of the queue lengths of
all the nodes in a wireless network with a forest
topology. Each packet is destined to one of the
roots (sinks) of the forest. We consider a time-
slotted system and a primary (or one-hop)
interference model. We characterize the
existence of causal sample-path optimal
2014

scheduling policies for this network topology
under this interference model. A causal sample-
path optimal scheduling policy is one for which
at each time-slot, and for any sample-path
traffic arrival pattern, the sum of the queue
lengths of all the nodes in the network is
minimum among all policies. We show that
such policies exist in restricted forest
structures, and that for any other forest
structure, there exists a traffic arrival pattern
for which no causal sample-path optimal policy
can exist. Surprisingly, we show that many
forest structures for which such policies exist
can be scheduled by converting the structure
into an equivalent linear network and
scheduling the equivalent linear network
according to the one-hop interference model.
The nonexistence of such policies in many
forest structures underscores the inherent
limitation of using sample-path optimality as a
performance metric and necessitates the need to
study other (relatively) weaker metrics of delay
performance.

4. PACK: Prediction-
Based Cloud
Bandwidth and
Cost Reduction
System
Abstract—In this paper, we present
PACK (Predictive ACKs), a novel end-to-end
traffic redundancy elimination (TRE) system,
designed for cloud computing customers.
Cloud-based TRE needs to apply a judicious
use of cloud resources so that the bandwidth
cost reduction combined with the additional
cost of TRE computation and storage would be
optimized. PACK’s main advantage is its
capability of offloading the cloud-server TRE
effort to end clients, thus minimizing the
processing costs induced by the TRE
algorithm. Unlike previous solutions, PACK
does not require the server to continuously
maintain clients’ status. This makes PACK
very suitable for pervasive computation
environments that combine client mobility and
server migration to maintain cloud elasticity.
PACK is based on a novel TRE technique,
which allows the client to use newly received
chunks to identify previously received chunk
chains, which in turn can be used as reliable
predictors to future transmitted chunks. We
2014

present a fully functional PACK
implementation, transparent to all TCP-based
applications and network devices. Finally, we
analyze PACK benefits for cloud users, using
traffic traces from various sources.
5. Secure Data
Retrieval for
Decentralized
Disruption-
Tolerant Military
Networks
Abstract—Mobile nodes in military
environments such as a battlefield or a hostile
region are likely to suffer from intermittent
network connectivity and frequent partitions.
Disruption-tolerant network (DTN)
technologies are becoming successful solutions
that allow wireless devices carried by soldiers
to communicate with each other and access the
confidential information or command reliably
by exploiting external storage nodes. Some of
the most challenging issues in this scenario are
the enforcement of authorization policies and
the policies update for secure data retrieval.
Ciphertext-policy attribute-based encryption
(CP-ABE) is a promising cryptographic
solution to the access control issues. However,
the problem of applying CP-ABE in
decentralized DTNs introduces several security
2014

and privacy challenges with regard to the
attribute revocation, key escrow, and
coordination of attributes issued from different
authorities. In this paper, we propose a secure
data retrieval scheme using CP-ABE for
decentralized DTNs where multiple key
authorities manage their attributes
independently. We demonstrate how to apply
the proposed mechanism to securely and
efficiently manage the confidential data
distributed in the disruption-tolerant military
network.
6. A Distributed
Control Law for
Load Balancing in
Content Delivery
Networks
In this paper, we face the challenging issue of
defining and implementing an effective law for
load balancing in Content Delivery Networks
(CDNs). We base our proposal on a formal
study of a CDN system, carried out through the
exploitation of a fluid flow model
characterization of the network of servers.
Starting from such characterization, we derive
and prove a lemma about the network queues
equilibrium. This result is then leveraged in
order to devise a novel distributed and time-
2013

continuous algorithm for load balancing, which
is also reformulated in a time-discrete version.
The discrete formulation of the proposed
balancing law is eventually discussed in terms
of its actual implementation in a real-world
scenario. Finally, the overall approach is
validated by means of simulations.
7. Achieving Efficient
Flooding by
Utilizing Link
Correlation in
Wireless Sensor
Networks
Although existing flooding protocols can
provide efficient and reliable communication in
wireless sensor networks on some level, further
performance improvement has been hampered
by the assumption of link independence, which
requires costly acknowledgments (ACKs) from
every receiver. In this paper, we present
collective flooding (CF), which exploits the
link correlation to achieve flooding reliability
using the concept of collective ACKs. CF
requires only 1-hop information at each node,
making the design highly distributed and
scalable with low complexity. We evaluate CF
extensively in real-world settings, using three
different types of testbeds: a single-hop
network with 20 MICAz nodes, a multihop
2013

network with 37 nodes, and a linear outdoor
network with 48 nodes along a 326-m-long
bridge. System evaluation and extensive
simulation show that CF achieves the same
reliability as state-of-the-art solutions while
reducing the total number of packet
transmission and the dissemination delay by
30%-50% and 35%-50%, respectively.
8. Complexity
Analysis and
Algorithm Design
for Advance
Bandwidth
Scheduling in
Dedicated
Networks
An increasing number of high-performance
networks provision dedicated channels through
circuit switching or MPLS/GMPLS techniques
to support large data transfer. The link
bandwidths in such networks are typically
shared by multiple users through advance
reservation, resulting in varying bandwidth
availability in future time. Developing efficient
scheduling algorithms for advance bandwidth
reservation has become a critical task to
improve the utilization of network resources
and meet the transport requirements of
application users. We consider an exhaustive
combination of different path and bandwidth
constraints and formulate four types of advance
2013

bandwidth scheduling problems, with the same
objective to minimize the data transfer end time
for a given transfer request with a prespecified
data size: fixed path with fixed bandwidth
(FPFB); fixed path with variable bandwidth
(FPVB); variable path with fixed bandwidth
(VPFB); and variable path with variable
bandwidth (VPVB). For VPFB and VPVB, we
further consider two subcases where the path
switching delay is negligible or nonnegligible.
We propose an optimal algorithm for each of
these scheduling problems except for FPVB
and VPVB with nonnegligible path switching
delay, which are proven to be NP-complete and
nonapproximable, and then tackled by
heuristics. The performance superiority of these
heuristics is verified by extensive experimental
results in a large set of simulated networks in
comparison to optimal and greedy strategies.
9. Efficient
Algorithms for
Neighbor
Discovery in
Neighbor discovery is an important first step in
the initialization of a wireless ad hoc network.
In this paper, we design and analyze several
algorithms for neighbor discovery in wireless
2013

Wireless Networks networks. Starting with a single-hop wireless
network of n nodes, we propose a Θ(nlnn)
ALOHA-like neighbor discovery algorithm
when nodes cannot detect collisions, and an
order-optimal Θ(n) receiver feedback-based
algorithm when nodes can detect collisions.
Our algorithms neither require nodes to have a
priori estimates of the number of neighbors nor
synchronization between nodes. Our algorithms
allow nodes to begin execution at different time
instants and to terminate neighbor discovery
upon discovering all their neighbors. We
finally show that receiver feedback can be used
to achieve a Θ(n) running time, even when
nodes cannot detect collisions. We then analyze
neighbor discovery in a general multihop
setting. We establish an upper bound of
O(Δlnn) on the running time of the ALOHA-
like algorithm, where Δ denotes the maximum
node degree in the network and nthe total
number of nodes. We also establish a lower
bound of Ω(Δ+lnn) on the running time of any
randomized neighbor discovery algorithm. Our

result thus implies that the ALOHA-like
algorithm is at most a factor min(Δ,lnn) worse
than optimal.
10. Semi-Random
Backoff: Towards
Resource
Reservation for
Channel Access in
Wireless LANs
This paper proposes a semi-random backoff
(SRB) method that enables resource reservation
in contention-based wireless LANs. The
proposed SRB is fundamentally different from
traditional random backoff methods because it
provides an easy migration path from random
backoffs to deterministic slot assignments. The
central idea of the SRB is for the wireless
station to set its backoff counter to a
deterministic value upon a successful packet
transmission. This deterministic value will
allow the station to reuse the time-slot in
consecutive backoff cycles. When multiple
stations with successful packet transmissions
reuse their respective time-slots, the collision
probability is reduced, and the channel
achieves the equivalence of resource
reservation. In case of a failed packet
transmission, a station will revert to the
standard random backoff method and probe for
2013

a new available time-slot. The proposed SRB
method can be readily applied to both 802.11
DCF and 802.11e EDCA networks with
minimum modification to the existing
DCF/EDCA implementations. Theoretical
analysis and simulation results validate the
superior performance of the SRB for small-
scale and heavily loaded wireless LANs. When
combined with an adaptive mechanism and a
persistent backoff process, SRB can also be
effective for large-scale and lightly loaded
wireless networks.
11. A Utility
Maximization
Frame work for
Fair and Efficient
Multicasting in
Multicarrier
Wireless Cellular
Networks
Multicast/broadcast is regarded as an efficient
technique for wireless cellular networks to
transmit a large volume of common data to
multiple mobile users simultaneously. To
guarantee the quality of service for each mobile
user in such single-hop multicasting, the base-
station transmitter usually adapts its data rate to
the worst channel condition among all users in
a multicast group. On one hand, increasing the
number of users in a multicast group leads to a
more efficient utilization of spectrum
2013

bandwidth, as users in the same group can be
served together. On the other hand, too many
users in a group may lead to unacceptably low
data rate at which the base station can transmit.
Hence, a natural question that arises is how to
efficiently and fairly transmit to a large number
of users requiring the same message. This
paper endeavors to answer this question by
studying the problem of multicasting over
multicarriers in wireless orthogonal frequency
division multiplexing (OFDM) cellular
systems. Using a unified utility maximization
framework, we investigate this problem in two
typical scenarios: namely, when users
experience roughly equal path losses and when
they experience different path losses,
respectively. Through theoretical analysis, we
obtain optimal multicast schemes satisfying
various throughput-fairness requirements in
these two cases. In particular, we show that the
conventional multicast scheme is optimal in the
equal-path-loss case regardless of the utility
function adopted. When users experience

different path losses, the group multicast
scheme, which divides the users almost equally
into many multicast groups and multicasts to
different groups of users over non overlapping
subcarriers, is optimal.
TECHNOLOGY: JAVA
DOMAIN: PARALLEL & DISTRIBUTED SYSTEM
1. Enabling
Trustworthy Service
Evaluation in
Service-Oriented
Mobile Social
Networks
In this paper, we propose a
Trustworthy Service Evaluation (TSE)
system to enable users to share service
reviews in service-oriented mobile social
networks (S-MSNs). Each service
provider independently maintains a TSE
for itself, which collects and stores users’
reviews about its services without
requiring any third trusted authority. The
service reviews can then be made
available to interested users in making
wise service selection decisions. We
identify three unique service review
2014

attacks, i.e., linkability, rejection, and
modification attacks, and develop
sophisticated security mechanisms for the
TSE to deal with these attacks.
Specifically, the basic TSE (bTSE)
enables users to distributedly and
cooperatively submit their reviews in an
integrated chain form by using
hierarchical and aggregate signature
techniques. It restricts the service
providers to reject, modify, or delete the
reviews. Thus, the integrity and
authenticity of reviews are improved.
Further, we extend the bTSE to a Sybil-
resisted TSE (SrTSE) to enable the
detection of two typical sybil attacks. In
the SrTSE, if a user generates multiple
reviews toward a vendor in a predefined
time slot with different pseudonyms, the
real identity of that user will be revealed.
Through security analysis and numerical
results, we show that the bTSE and the
SrTSE effectively resist the service review

attacks and the SrTSE additionally detects
the sybil attacks in an efficient manner.
Through performance evaluation, we show
that the bTSE achieves better performance
in terms of submission rate and delay than
a service review system that does not
adopt user cooperation.
2. A Tag Encoding
Scheme against
Pollution Attack to
Linear Network
Coding
Network coding allows
intermediate nodes to encode data packets
to improve network throughput and
robustness. However, it increases the
propagation speed of polluted data packets
if a malicious node injects fake data
packets into the network, which degrades
the bandwidth efficiency greatly and leads
to incorrect decoding at sinks. In this
paper, insights on new mathematical
relations in linear network coding are
presented and a key predistribution-based
tag encoding scheme KEPTE is proposed,
which enables all intermediate nodes and
sinks to detect the correctness of the
received data packets. Furthermore, the
2014

security of KEPTE with regard to
pollution attack and tag pollution attack is
quantitatively analyzed. The performance
of KEPTE is competitive in terms of: 1)
low computational complexity; 2) the
ability that all intermediate nodes and
sinks detect pollution attack; 3) the ability
that all intermediate nodes and sinks
detect tag pollution attack; and 4) high
fault-tolerance ability. To the best of our
knowledge, the existing key
predistribution-based schemes aiming at
pollution detection can only achieve at
most three points as described above.
Finally, discussions on the application of
KEPTE to practical network coding are
also presented.
3. Exploiting Service
Similarity for
Privacy in Location-
Based Search
Queries
Location-based applications utilize
the positioning capabilities of a mobile
device to determine the current location of
a user, and customize query results to
include neighboring points of interests.
However, location knowledge is often
2014

perceived as personal information. One of
the immediate issues hindering the wide
acceptance of location-based applications
is the lack of appropriate methodologies
that offer fine grain privacy controls to a
user without vastly affecting the usability
of the service. While a number of privacy-
preserving models and algorithms have
taken shape in the past few years, there is
an almost universal need to specify one’s
privacy requirement without
understanding its implications on the
service quality. In this paper, we propose a
user-centric location based service
architecture where a user can observe the
impact of location inaccuracy on the
service accuracy before deciding the geo-
coordinates to use in a query. We
construct a local search application based
on this architecture and demonstrate how
meaningful information can be exchanged
between the user and the service provider
to allow the inference of contours

depicting the change in query results
across a geographic area. Results indicate
the possibility of large default privacy
regions (areas of no change in result set)
in such applications.
4. Network Coding
Aware Cooperative
MAC Protocol for
Wireless Ad Hoc
Networks
Cooperative communication, which
utilizes neighboring nodes to relay the
overhearing information, has been employed
as an effective technique to deal with the
channel fading and to improve the network
performances. Network coding, which
combines several packets together for
transmission, is very helpful to reduce the
redundancy at the network and to increase the
overall throughput. Introducing network
coding into the cooperative retransmission
process enables the relay node to assist other
nodes while serving its own traffic
simultaneously. To leverage the benefits
brought by both of them, an efficient Medium
Access Control (MAC) protocol is needed. In
this paper, we propose a novel network coding
aware cooperative MAC protocol, namely
2014

NCAC-MAC, for wireless ad hoc networks.
The design objective of NCAC-MAC is to
increase the throughput and reduce the delay.
Simulation results reveal that NCAC-MAC
can improve the network performance under
general circumstances comparing with two
benchmarks.
5. A Probabilistic
Misbehavior
Detection Scheme
toward Efficient
Trust Establishment
in Delay-Tolerant
Networks
Abstract—Malicious and selfish behaviors
represent a serious threat against routing in
delay/disruption tolerant networks (DTNs).
Due to the unique network characteristics,
designing a misbehavior detection scheme in
DTN is regarded as a great challenge. In this
paper, we propose iTrust, a probabilistic
misbehavior detection scheme, for secure
DTN routing toward efficient trust
establishment. The basic idea of iTrust is
introducing a periodically available Trusted
Authority (TA) to judge the node’s behavior
based on the collected routing evidences and
probabilistically checking. We model iTrust
as the inspection game and use game
theoretical analysis to demonstrate that, by
2014

setting an appropriate investigation
probability, TA could ensure the security of
DTN routing at a reduced cost. To further
improve the efficiency of the proposed
scheme, we correlate detection probability
with a node’s reputation, which allows a
dynamic detection probability determined by
the trust of the users. The extensive analysis
and simulation results demonstrate the
effectiveness and efficiency of the proposed
scheme.
6. A System for Denial-
of-Service Attack
Detection Based on
Multivariate
Correlation Analysis
Abstract—Interconnected systems,
such as Web servers, database servers, cloud
computing servers and so on, are now under
threads from network attackers. As one of
most common and aggressive means, denial-
of-service (DoS) attacks cause serious impact
on these computing systems. In this paper, we
present a DoS attack detection system that
uses multivariate correlation analysis (MCA)
for accurate network traffic characterization
by extracting the geometrical correlations
between network traffic features. Our MCA-
2014

based DoS attack detection system employs
the principle of anomaly based detection in
attack recognition. This makes our solution
capable of detecting known and unknown
DoS attacks effectively by learning the
patterns of legitimate network traffic only.
Furthermore, a triangle-area-based technique
is proposed to enhance and to speed up the
process of MCA. The effectiveness of our
proposed detection system is evaluated using
KDD Cup 99 data set, and the influences of
both non-normalized data and normalized data
on the performance of the proposed detection
system are examined. The results show that
our system outperforms two other previously
developed state-of-the-art approaches in terms
of detection accuracy.
7. ReDS: A Framework
for Reputation-
Enhanced DHTs
Abstract—Distributed hash tables
(DHTs), such as Chord and Kademlia, offer
an efficient means to locate resources in peer-
to-peer networks. Unfortunately, malicious
nodes on a lookup path can easily subvert
such queries. Several systems, including Halo
2014

(based on Chord) and Kad (based on
Kademlia), mitigate such attacks by using
redundant lookup queries. Much greater
assurance can be provided; we present
Reputation for Directory Services (ReDS), a
framework for enhancing lookups in
redundant DHTs by tracking how well other
nodes service lookup requests. We describe
how the ReDS technique can be applied to
virtually any redundant DHT including Halo
and Kad. We also study the collaborative
identification and removal of bad lookup
paths in a way that does not rely on the
sharing of reputation scores, and we show that
such sharing is vulnerable to attacks that make
it unsuitable for most applications of ReDS.
Through extensive simulations, we
demonstrate that ReDS improves lookup
success rates for Halo and Kad by 80 percent
or more over a wide range of conditions, even
against strategic attackers attempting to game
their reputation scores and in the presence of
node churn.

8. A Secure Payment
Scheme with Low
Communication and
Processing Over
head for Multihop
wireless Networks
We propose RACE, a report-based payment
scheme for multihop wireless networks to
stimulate node cooperation, regulate packet
transmission, and enforce fairness. The nodes
submit lightweight payment reports (instead
of receipts) to the accounting center (AC) and
temporarily store undeniable security tokens
called Evidences. The reports contain the
alleged charges and rewards without security
proofs, e.g., signatures. The AC can verify the
payment by investigating the consistency of
the reports, and clear the payment of the fair
reports with almost no processing overhead or
cryptographic operations. For cheating
reports, the Evidences are requested to
identify and evict the cheating nodes that
submit incorrect reports. Instead of requesting
the Evidences from all the nodes participating
in the cheating reports, RACE can identify the
cheating nodes with requesting few
Evidences. Moreover, Evidence aggregation
technique is used to reduce the Evidences'
storage area. Our analytical and simulation
2013

results demonstrate that RACE requires much
less communication and processing overhead
than the existing receipt-based schemes with
acceptable payment clearance delay and
storage area. This is essential for the effective
implementation of a payment scheme because
it uses micropayment and the overhead cost
should be much less than the payment value.
Moreover, RACE can secure the payment and
precisely identify the cheating nodes without
false accusations.
9. Cluster-Based
Certificate
Revocation with
Vindication
Capability for
Mobile Ad Hoc
Networks
Mobile ad hoc networks (MANETs) have
attracted much attention due to their mobility
and ease of deployment. However, the
wireless and dynamic natures render them
more vulnerable to various types of security
attacks than the wired networks. The major
challenge is to guarantee secure network
services. To meet this challenge, certificate
revocation is an important integral component
to secure network communications. In this
paper, we focus on the issue of certificate
revocation to isolate attackers from further
2013

participating in network activities. For quick
and accurate certificate revocation, we
propose the Cluster-based Certificate
Revocation with Vindication Capability
(CCRVC) scheme. In particular, to improve
the reliability of the scheme, we recover the
warned nodes to take part in the certificate
revocation process; to enhance the accuracy,
we propose the threshold-based mechanism to
assess and vindicate warned nodes as
legitimate nodes or not, before recovering
them. The performances of our scheme are
evaluated by both numerical and simulation
analysis. Extensive results demonstrate that
the proposed certificate revocation scheme is
effective and efficient to guarantee secure
communications in mobile ad hoc networks.
10. Fault Tolerance in
Distributed Systems
Using Fused Data
Structures
Replication is the prevalent solution to
tolerate faults in large data structures hosted
on distributed servers. To tolerate f crash
faults (dead/unresponsive data structures)
among n distinct data structures, replication
requires f + 1 replicas of each data structure,
2013

resulting in nf additional backups. We present
a solution, referred to as fusion that uses a
combination of erasure codes and selective
replication to tolerate f crash faults using just f
additional fused backups. We show that our
solution achieves O(n) savings in space over
replication. Further, we present a solution to
tolerate f Byzantine faults (malicious data
structures), that requires only nf + f backups
as compared to the 2nf backups required by
replication. We explore the theory of fused
backups and provide a library of such backups
for all the data structures in the Java
Collection Framework. The theoretical and
experimental evaluation confirms that the
fused backups are space-efficient as compared
to replication, while they cause very little
overhead for normal operation. To illustrate
the practical usefulness of fusion, we use
fused backups for reliability in Amazon's
highly available key-value store, Dynamo.
While the current replication - based solution
uses 300 backup structures, we present a

solution that only requires 120 backup
structures. This results in savings in space as
well as other resources such as power.
11. Flexible Symmetrical
Global - Snapshot
Algorithms for Large
-Scale Distributed
Systems
Most existing global-snapshot algorithms in
distributed systems use control messages to
coordinate the construction of a global
snapshot among all processes. Since these
algorithms typically assume the underlying
logical overlay topology is fully connected,
the number of control messages exchanged
among the whole processes is proportional to
the square of number of processes, resulting in
higher possibility of network congestion.
Hence, such algorithms are neither efficient
nor scalable for a large-scale distributed
system composed of a huge number of
processes. Recently, some efforts have been
presented to significantly reduce the number
of control messages, but doing so incurs
higher response time instead. In this paper, we
propose an efficient global-snapshot algorithm
able to let every process finish its local
snapshot in a given number of rounds.
2013

Particularly, such an algorithm allows a
tradeoff between the response time and the
message complexity. Moreover, our global-
snapshot algorithm is symmetrical in the sense
that identical steps are executed by every
process. This means that our algorithm is able
to achieve better workload balance and less
network congestion. Most importantly, based
on our framework, we demonstrate that the
minimum number of control messages
required by a symmetrical global-snapshot
algorithm is Omega (Nlog N), where N is the
number of processes. Finally, we also assume
non-FIFO channels.
12. High Performance
Resource Allocation
Strategies for
Computational
Economies
Utility computing models have long been the
focus of academic research, and with the
recent success of commercial cloud providers,
computation and storage is finally being
realized as the fifth utility. Computational
economies are often proposed as an efficient
means of resource allocation, however
adoption has been limited due to a lack of
performance and high overheads. In this
2013

paper, we address the performance limitations
of existing economic allocation models by
defining strategies to reduce the failure and
reallocation rate, increase occupancy and
thereby increase the obtainable utilization of
the system. The high-performance resource
utilization strategies presented can be used by
market participants without requiring dramatic
changes to the allocation protocol. The
strategies considered include overbooking,
advanced reservation, just-in-time bidding,
and using substitute providers for service
delivery. The proposed strategies have been
implemented in a distributed meta scheduler
and evaluated with respect to Grid and cloud
deployments. Several diverse synthetic
workloads have been used to quantity both the
performance benefits and economic
implications of these strategies.
13. Optimal Client-
Server Assignment
for Internet
Distributed Systems
We investigate an underlying mathematical
model and algorithms for optimizing the
performance of a class of distributed systems
over the Internet. Such a system consists of a
2013

large number of clients who communicate
with each other indirectly via a number of
intermediate servers. Optimizing the overall
performance of such a system then can be
formulated as a client-server assignment
problem whose aim is to assign the clients to
the servers in such a way to satisfy some pre
specified requirements on the communication
cost and load balancing. We show that 1) the
total communication load and load balancing
are two opposing metrics, and consequently,
their tradeoff is inherent in this class of
distributed systems; 2) in general, finding the
optimal client-server assignment for some pre
specified requirements on the total load and
load balancing is NP-hard, and therefore; 3)
we propose a heuristic via relaxed convex
optimization for finding the approximate
solution. Our simulation results indicate that
the proposed algorithm produces superior
performance than other heuristics, including
the popular Normalized Cuts algorithm.
14. Scheduling Sensor The network traffic pattern of continuous 2013

Data Collection with
Dynamic Traffic
Patterns
sensor data collection often changes
constantly over time due to the exploitation of
temporal and spatial data correlations as well
as the nature of condition - based monitoring
applications. This paper develops a novel
TDMA schedule that is capable of efficiently
collecting sensor data for any network traffic
pattern and is thus well suited to continuous
data collection with dynamic traffic patterns.
Following this schedule, the energy consumed
by sensor nodes for any traffic pattern is very
close to the minimum required by their
workloads given in the traffic pattern. The
schedule also allows the base station to
conclude data collection as early as possible
according to the traffic load, thereby reducing
the latency of data collection. Experimental
results using real-world data traces show that,
compared with existing schedules that are
targeted on a fixed traffic pattern, our
proposed schedule significantly improves the
energy efficiency and time efficiency of
sensor data collection with dynamic traffic

patterns.
TECHNOLOGY: JAVA
DOMAIN: SOFTWARE ENGINEERING
1. Ant Colony
Optimization for
Software Project
Scheduling and
Staffing with an
Event-Based
Scheduler
Research into developing effective computer
aided techniques for planning software projects
is important and challenging for software
engineering. Different from projects in other
fields, software projects are people-intensive
activities and their related resources are mainly
human resources. Thus, an adequate model for
software project planning has to deal with not
only the problem of project task scheduling but
also the problem of human resource allocation.
But as both of these two problems are difficult,
existing models either suffer from a very large
search space or have to restrict the flexibility of
human resource allocation to simplify the
model. To develop a flexible and effective
model for software project planning, this paper
develops a novel approach with an event-based
2013

scheduler (EBS) and an ant colony
optimization (ACO) algorithm. The proposed
approach represents a plan by a task list and a
planned employee allocation matrix. In this
way, both the issues of task scheduling and
employee allocation can be taken into account.
In the EBS, the beginning time of the project,
the time when resources are released from
finished tasks, and the time when employees
join or leave the project are regarded as events.
The basic idea of the EBS is to adjust the
allocation of employees at events and keep the
allocation unchanged at nonevents. With this
strategy, the proposed method enables the
modeling of resource conflict and task
preemption and preserves the flexibility in
human resource allocation. To solve the
planning problem, an ACO algorithm is further
designed. Experimental results on 83 instances
demonstrate that the proposed method is very
promising.

TECHNOLOGY: DOTNET
DOMAIN: CLOUD COMPUTING
1. A Novel Economic
Sharing Model in a
Federation of Selfish
Cloud Providers
Abstract—This paper presents a novel
economic model to regulate capacity sharing
in a federation of hybrid cloud providers
(CPs). The proposed work models the
interactions among the CPs as a repeated
game among selfish players that aim at
maximizing their profit by selling their unused
capacity in the spot market but are uncertain
of future workload fluctuations. The proposed
work first establishes that the uncertainty in
future revenue can act as a participation
incentive to sharing in the repeated game. We,
then, demonstrate how an efficient sharing
strategy can be obtained via solving a simple
dynamic programming problem. The obtained
strategy is a simple update rule that depends
only on the current workloads and a single
variable summarizing past interactions. In
2014

contrast to existing approaches, the model
incorporates historical and expected future
revenue as part of the virtual machine (VM)
sharing decision. Moreover, these decisions
are enforced neither by a centralized broker
nor by predefined agreements. Rather, the
proposed model employs a simple grim
trigger strategy where a CP is threatened by
the elimination of future VM hosting by other
CPs. Simulation results demonstrate the
performance of the proposed model in terms
of the increased profit and the reduction in the
variance in the spot market VM availability
and prices.
2. A UCONABC
Resilient
Authorization
Evaluation for Cloud
Computing
The business-driven access control
used in cloud computing is not well suited for
tracking fine-grained user service
consumption. UCONABC applies continuous
authorization reevaluation, which requires
usage accounting that enables fine-grained
access control for cloud computing. However,
it was not designed to work in distributed and
dynamic authorization environments like
2014

those present in cloud computing. During a
continuous (periodical) reevaluation, an
authorization exception condition, disparity
among usage accounting and authorization
attributes may occur. This proposal aims to
provide resilience to the UCONABC
continuous authorization reevaluation, by
dealing with individual exception conditions
while maintaining a suitable access control in
the cloud environment. The experiments made
with a proof-of-concept prototype show a set
of measurements for an application scenario
(e-commerce) and allows for the identification
of exception conditions in the authorization
reevaluation.
3. Distributed,
Concurrent, and
Independent Access
to Encrypted Cloud
Databases
Abstract—Placing critical data in the
hands of a cloud provider should come with
the guarantee of security and availability for
data at rest, in motion, and in use. Several
alternatives exist for storage services, while
data confidentiality solutions for the database
as a service paradigm are still immature. We
propose a novel architecture that integrates
2014

cloud database services with data
confidentiality and the possibility of executing
concurrent operations on encrypted data. This
is the first solution supporting geographically
distributed clients to connect directly to an
encrypted cloud database, and to execute
concurrent and independent operations
including those modifying the database
structure. The proposed architecture has the
further advantage of eliminating intermediate
proxies that limit the elasticity, availability,
and scalability properties that are intrinsic in
cloud-based solutions. The efficacy of the
proposed architecture is evaluated through
theoretical analyses and extensive
experimental results based on a prototype
implementation subject to the TPC-C standard
benchmark for different numbers of clients
and network latencies.
4. Key-Aggregate
Cryptosystem for
Scalable Data
Sharing in Cloud
Abstract—Data sharing is an
important functionality in cloud storage. In
this paper, we show how to securely,
efficiently, and flexibly share data with others
2014

Storage in cloud storage. We describe new public-key
cryptosystems that produce constant-size
cipher texts such that efficient delegation of
decryption rights for any set of cipher texts
are possible. The novelty is that one can
aggregate any set of secret keys and make
them as compact as a single key, but
encompassing the power of all the keys being
aggregated. In other words, the secret key
holder can release a constant-size aggregate
key for flexible choices of ciphertext set in
cloud storage, but the other encrypted files
outside the set remain confidential. This
compact aggregate key can be conveniently
sent to others or be stored in a smart card with
very limited secure storage. We provide
formal security analysis of our schemes in the
standard model. We also describe other
application of our schemes. In particular, our
schemes give the first public-key patient-
controlled encryption for flexible hierarchy,
which was yet to be known.

TECHNOLOGY: DOTNET
DOMAIN: DATA MINING
1. A Group Incremental
Approach to Feature
Selection Applying
Rough Set Technique
Many real data increase
dynamically in size. This phenomenon
occurs in several fields including
economics, population studies, and
medical research. As an effective and
efficient mechanism to deal with such
data, incremental technique has been
proposed in the literature and attracted
much attention, which stimulates the result
in this paper. When a group of objects are
added to a decision table, we first
introduce incremental mechanisms for
three representative information entropies
and then develop a group incremental
rough feature selection algorithm based on
information entropy. When multiple
objects are added to a decision table, the
algorithm aims to find the new feature
subset in a much shorter time.
Experiments have been carried out on
eight UCI data sets and the experimental
results show that the algorithm is effective
and efficient.
2014
2. Consensus-Based Abstract—In this paper, we tackle a novel 2014

Ranking of
Multivalued Objects:
A Generalized Borda
Count Approach
problem of ranking multivalued objects,
where an object has multiple instances in a
multidimensional space, and the number of
instances per object is not fixed. Given an ad
hoc scoring function that assigns a score to a
multidimensional instance, we want to rank a
set of multivalued objects. Different from the
existing models of ranking uncertain and
probabilistic data, which model an object as a
random variable and the instances of an object
are assumed exclusive, we have to capture the
coexistence of instances here. To tackle the
problem, we advocate the semantics of
favoring widely preferred objects instead of
majority votes, which is widely used in many
elections and competitions. Technically, we
borrow the idea from Borda Count (BC), a
well-recognized method in consensus-based
voting systems. However, Borda Count cannot
handle multivalued objects of inconsistent
cardinality, and is costly to evaluate top k
queries on large multidimensional data sets.
To address the challenges, we extend and

generalize Borda Count to quantile-based
Borda Count, and develop efficient
computational methods with comprehensive
cost analysis. We present case studies on real
data sets to demonstrate the effectiveness of
the generalized Borda Count ranking, and use
synthetic and real data sets to verify the
efficiency of our computational method.
3. Rough Sets, Kernel
Set, and
Spatiotemporal
Outlier Detection
Abstract—Nowadays, the high availability of
data gathered from wireless sensor networks
and telecommunication systems has drawn the
attention of researchers on the problem of
extracting knowledge from spatiotemporal
data. Detecting outliers which are grossly
different from or inconsistent with the
remaining spatiotemporal data set is a major
challenge in real-world knowledge discovery
and data mining applications. In this paper, we
deal with the outlier detection problem in
spatiotemporal data and describe a rough set
approach that finds the top outliers in an
unlabeled spatiotemporal data set. The
proposed method, called Rough Outlier Set
Extraction (ROSE), relies on a rough set
theoretic representation of the outlier set using
the rough set approximations, i.e., lower and
upper approximations. We have also
introduced a new set, named Kernel Set, that
2014

is a subset of the original data set, which is
able to describe the original data set both in
terms of data structure and of obtained results.
Experimental results on real-world data sets
demonstrate the superiority of ROSE, both in
terms of some quantitative indices and outliers
detected, over those obtained by various rough
fuzzy clustering algorithms and by the state-
of-the-art outlier detection methods. It is also
demonstrated that the kernel set is able to
detect the same outliers set but with less
computational time.
4. Discovering
Temporal Change
Patterns in the
Presence of
Taxonomies
Frequent items mining is a widely exploratory
technique that focuses on discovering
recurrent correlations among data. The
steadfast evolution of markets and business
environments prompts the need of data mining
algorithms to discover significant correlation
changes in order to reactively suit product and
service provision to customer needs. Change
mining, in the context of frequent item sets,
focuses on detecting and reporting significant
changes in the set of mined item sets from one
time period to another. The discovery of
frequent generalized item sets, i.e., item sets
that 1) frequently occur in the source data, and
2013

2) provide a high-level abstraction of the
mined knowledge, issues new challenges in
the analysis of item sets that become rare, and
thus are no longer extracted, from a certain
point. This paper proposes a novel kind of
dynamic pattern, namely the History
Generalized Pattern (HiGen), that represents
the evolution of an item set in consecutive
time periods, by reporting the information
about its frequent generalizations
characterized by minimal redundancy (i.e.,
minimum level of abstraction) in case it
becomes infrequent in a certain time period.
To address HiGen mining, it proposes HiGen
Miner, an algorithm that focuses on avoiding
item set mining followed by post processing
by exploiting a support-driven item set
generalization approach. To focus the
attention on the minimally redundant frequent
generalizations and thus reduce the amount of
the generated patterns, the discovery of a
smart subset of HiGens, namely the Non-
redundant HiGens, is addressed as well.

Experiments performed on both real and
synthetic datasets show the efficiency and the
effectiveness of the proposed approach as well
as its usefulness in a
real application context.
5. Information-
Theoretic Outlier
Detection for Large-
Scale Categorical
Data
Outlier detection can usually be considered as
a pre-processing step for locating, in a data
set, those objects that do not conform to well-
defined notions of expected behavior. It is
very
important in data mining for discovering
novel or rare events, anomalies, vicious
actions, exceptional phenomena, etc. We are
investigating outlier detection for categorical
data sets. This problem is especially
challenging because of the difficulty of
defining a meaningful similarity measure for
categorical data. In this paper, we propose a
formal definition of outliers and an
optimization model of outlier detection, via a
new concept of holo entropy that takes both
entropy and total correlation into
consideration. Based on this model, we define
2013

a function for the outlier factor of an object
which is solely determined by the object itself
and can be updated efficiently. We propose
two practical 1
-parameter outlier detection methods, named
ITB-SS and ITB-SP, which require no user-
defined parameters for deciding whether an
object is an outlier. Users need only provide
the number of outliers they want to detect.
Experimental results show that ITB-SS and
ITB-SP are more effective and efficient than
mainstream methods and can be used to deal
with both large and high-dimensional data sets
where existing algorithms fail.
6. Robust Module-
Based Data
Management
The current trend for building an ontology-
based data management system (DMS) is to
capitalize on efforts made to design a
preexisting well-established DMS (a reference
system). The method amounts to extracting
from the reference DMS a piece of schema
relevant to the new application needs-a
module
-, possibly personalizing it with extra
2013

constraints w.r.t. the application under
construction, and then managing a data set
using the resulting schema. In this paper, we
extend the existing definitions of modules and
we introduce novel properties of robustness
that provide means for checking easily that a
robust module-based DMS evolves safely
w.r.t. both the schema and the data of the
reference DMS. We carry out our
investigations in the setting of description
logics which underlie modern ontology
languages, like RDFS, OWL, and OWL2 from
W3C. Notably, we focus on the DL-liteA
dialect of the DL-lite family, which
encompasses the foundations of the QL
profile of OWL2 (i.e., DL-liteR): the W3C
recommendation for efficiently managing
large data sets.
7. Protecting Sensitive
Labels in Social
Network Data
Anonymization
Privacy is one of the major concerns when
publishing or sharing social network data for
social science research and business analysis.
Recently, researchers have developed privacy
models similar to k-anonymity to prevent
2013

node reidentification through structure
information. However, even when these
privacy models are enforced, an attacker may
still be able to infer one's private information
if a group of nodes largely share the same
sensitive labels (i.e., attributes). In other
words, the label-node relationship is not well
protected by pure structure anonymization
methods. Furthermore, existing approaches,
which rely on edge editing or node clustering,
may significantly alter key graph properties.
In this paper, we define a k-degree-l-diversity
anonymity model that considers the protection
of structural information as well as sensitive
labels of individuals. We further propose a
novel anonymization methodology based on
adding noise nodes. We develop a new
algorithm by adding noise nodes into the
original graph with the consideration of
introducing the least distortion to graph
properties. Most importantly, we provide a
rigorous analysis of the theoretical bounds on
the number of noise nodes added and their

impacts on an important graph property. We
conduct extensive experiments to evaluate the
effectiveness of the proposed technique
TECHNOLOGY: DOTNET
DOMAIN: PARALLEL & DISTRIBUTED SYSTEM
1. Behavioral
Malware Detection
in Delay Tolerant
Networks
Abstract—The delay-tolerant-network (DTN)
model is becoming a viable communication
alternative to the traditional infrastructural
model for modern mobile consumer electronics
equipped with short-range communication
technologies such as Bluetooth, NFC, and Wi-
Fi Direct. Proximity malware is a class of
malware that exploits the opportunistic contacts
and distributed nature of DTNs for
propagation. Behavioral characterization of
malware is an effective alternative to pattern
matching in detecting malware, especially
when dealing with polymorphic or obfuscated
malware. In this paper, we first propose a
general behavioral characterization of
2014

proximity malware which based on naive
Bayesian model, which has been successfully
applied in non-DTN settings such as filtering
email spams and detecting botnets. We identify
two unique challenges for extending Bayesian
malware detection to DTNs (“insufficient
evidence versus evidence collection risk” and
“filtering false evidence sequentially and
distributedly”), and propose a simple yet
effective method, look ahead, to address the
challenges. Furthermore, we propose two
extensions to look ahead, dogmatic filtering,
and adaptive look ahead, to address the
challenge of “malicious nodes sharing false
evidence.” Real mobile network traces are used
to verify the effectiveness of the proposed
methods.
2. LocaWard: A
Security and
Privacy Aware
Location-Based
Rewarding System
Abstract—The proliferation of mobile
devices has driven the mobile marketing to
surge in the past few years. Emerging as a new
type of mobile marketing, mobile location-
based services (MLBSs) have attracted intense
attention recently. Unfortunately, current
2014

MLBSs have a lot of limitations and raise many
concerns, especially about system security and
users’ privacy. In this paper, we propose a new
location-based rewarding system, called
LocaWard, where mobile users can collect
location-based tokens from token distributors,
and then redeem their gathered tokens at token
collectors for beneficial rewards. Tokens act as
virtual currency. The token distributors and
collectors can be any commercial entities or
merchants that wish to attract customers
through such a promotion system, such as
stores, restaurants, and car rental companies.
We develop a security and privacy aware
location-based rewarding protocol for the
LocaWard system, and prove the completeness
and soundness of the protocol. Moreover, we
show that the system is resilient to various
attacks and mobile users’ privacy can be well
protected in the meantime. We finally
implement the system and conduct extensive
experiments to validate the system efficiency in
terms of computation, communication, energy

consumption, and storage costs.
3. Power Cost
Reduction in
Distributed Data
Centers: A Two-
Time-Scale
Approach for
Delay Tolerant
Workloads
Abstract—This paper considers a
stochastic optimization approach for job
scheduling and server management in large-
scale, geographically distributed data centers.
Randomly arriving jobs are routed to a choice
of servers. The number of active servers
depends on server activation decisions that are
updated at a slow time scale, and the service
rates of the servers are controlled by power
scaling decisions that are made at a faster time
scale. We develop a two-time-scale decision
strategy that offers provable power cost and
delay guarantees. The performance and
robustness of the approach is illustrated
through simulations.
2014
4. Traffic Pattern-
Based Content
Leakage Detection
for Trusted Content
Delivery Networks
Abstract—Due to the increasing
popularity of multimedia streaming
applications and services in recent years, the
issue of trusted video delivery to prevent
undesirable content-leakage has, indeed,
become critical. While preserving user privacy,
conventional systems have addressed this issue
2014

by proposing methods based on the observation
of streamed traffic throughout the network.
These conventional systems maintain a high
detection accuracy while coping with some of
the traffic variation in the network (e.g.,
network delay and packet loss), however, their
detection performance substantially degrades
owing to the significant variation of video
lengths. In this paper, we focus on overcoming
this issue by proposing a novel content-leakage
detection scheme that is robust to the variation
of the video length. By comparing videos of
different lengths, we determine a relation
between the length of videos to be compared
and the similarity between the compared
videos. Therefore, we enhance the detection
performance of the proposed scheme even in an
environment subjected to variation in length of
video. Through a testbed experiment, the
effectiveness of our proposed scheme is
evaluated in terms of variation of video length,
delay variation, and packet loss.

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