Ijarcet vol-2-issue-3-951-956

ISSN: 2278 – 1323
International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)
Volume 2, Issue 3, March 2013
951
All Rights Reserved © 2013 IJARCET

Abstract— Cloud computing prevails over the whole world
which makes the mankind to rely more on a number of online
storage systems to back up data or for using it in real time which
gives an anywhere, anytime access. Thus lot of sensitive data will
be stored into the cloud. As the user’s data are stored and
maintained out of user’s premises, these services bring with it,
concerns of security vulnerabilities for all the services provided
by them. There is a lot of research being done to point out the
issues with the service providers and cloud security in general.
This paper provides a privacy-preserving data integrity
protection by enabling public auditability for cloud storage and
implements a scalable framework that addresses the
construction of an interactive audit protocol to prevent the
fraudulence of prover and the leakage of verified data in cloud
storage by reducing the overhead in computation,
communication and storage. Thus it provides a lightweight,
efficient and privacy –preserving auditing scheme which will
also identifies the corrupted data. The framework is based on an
interactive PDP protocol that uses the challenge – response
algorithms and a verification protocol. The protocol supports
dynamic operation that allows the granted users to perform
insertion, deletion and updation. The protocol allows a Third
party Auditor who works on behalf of the Data owner who has a
large amount of data to be stored in the cloud, to manage or
monitor outsourced data. The experimental results show that
the protocol is efficient, lightweight and privacy-preserving.
Index Terms—— Auditing, Cloud Computing, Cloud
Storage, Privacy.
I. INTRODUCTION
Cloud computing is the buzz in the Information Technology
world and is presently appears as a hot topic due to its abilities
to offer flexible, dynamic IT infrastructures; QoS guaranteed
computing environments and configurable software services.
National Institute of Standards and technology, agency of US
department of Commerce, defines cloud computing as a
model for enabling convenient, on-demand network access to
a shared pool of configurable computing resources (e.g.,
networks, servers, storage, applications, and services) that can
Manuscript received March, 2013.
Neethu Mariam Joseph, Information Technology, Karunya University.,
Coimbatore, India.
Esther Daniel, Information Technology, Karunya University.,
Coimbatore, India,
Vasanthi N A, Department of Computer Science and Engineering,
Nehru Institute of Engineering and Technology, Coimbatore, India.
be rapidly provisioned and released with minimal
management effort or service provider interaction [1]. The
indispensable characteristics of cloud computing includes
on-demand self-service, resource pooling, broad network
access, rapid elasticity and measured service. Cloud model
promotes availability and is composed of three service models
and four deployment models. The three service models
includes the software as a service (SaaS) which refers to
providing on demand applications over the Internet, platform
as a service (PaaS) which refers to providing platform layer
resources, including operating system support and software
development frameworks, and finally the infrastructure as a
service (IaaS) which refers to on-demand provisioning of
infrastructural resources, usually in terms of VMs. Examples
of SaaS providers include Salesforce.com , Rackspace and
SAP Business ByDesign. Examples of PaaS providers include
Google App Engine, Microsoft Windows Azure and
Force.com. IaaS providers include Amazon EC2, GoGrid and
Flexiscale [1]. The four deployment models are the Public
cloud in which service providers offer their resources as
services to the general public, private cloud also known as
internal clouds, are designed for exclusive use by a single
organization and hybrid cloud is a combination of public and
private cloud models that acts on the limitations of each
approach.
One of the first cloud offerings was cloud storage and it
remains a popular answer. Cloud storage is a model of
networked online storage in which the data is stored in
virtualized pools of storage that are generally being hosted by
the third parties. Cloud storage allows data stored remotely to
be temporarily cached on mobile phones, desktop computers,
or other Internet-linked devices.
There are several advantages for Cloud computing that it
provides several salient features unlike from traditional
service computing, which includes multi-tenancy, shared
resource pooling, geo-distribution and ubiquitous network
access, service oriented, dynamic resource provisioning,
self-organizing, utility-based pricing, high scalability , agility,
high availability and reliability, reduces the number of
hardware components, reduce energy costs for running and
cooling hardware components, reduce run time and response
time, minimizing the risk of deploying physical infrastructure,
lowering the cost of entry, increasing the pace of innovation
and more [1]-[3].
A Scalable Privacy-Preserving Verification
Correctness Protocol to Identify Corrupted Data
in Cloud Storage
Neethu Mariam Joseph, Esther Daniel, Vasanthi N A

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952
II. RELATED WORK
There has been an extensive number of works done on
untrusted outsourced storage. Employment of cryptographic
hash function is one way to enforce the integrity control.
Using Merkle tree as the underlying data structure,
Yumerefendi and Chase proposed a solution for authenticated
network storage. But their handling of updates is
computationally expensive. To check the integrity of stored
data without download, some researchers have proposed two
basic approaches called provable data possession (PDP)
(Ateniese et al., 2007) and proofs of retrievability (POR)
(Juels, 2007). Ateniese et al. (2007) first proposed the PDP
model for ensuring possession of files on untrusted storages
and provided an RSA-based scheme for the static case that
achieves O(1) communication costs. They also proposed a
publicly verifiable version, which allows anyone, not just the
owner, to challenge the servers for data possession. This
property greatly extends application areas of PDP protocol
due to the separation of data owners and the authorized users.
The “Provable Data Possession” (PDP) model in [9] ensures
the possession of data files on untrusted storages. It uses a
RSA based homomorphic linear authenticator for auditing
outsourced data, but this model leaks the data to external
auditors and hence was not provably privacy preserving. Juels
et.al in [8] describes a “Proof of Retrievability” (PoR) model,
where spot-checking and error correcting codes are used in
order to ensure the possession and retrievability. But this
approach works only with encrypted data. Improved versions
of PoR protocols had been proposed which guarantees private
auditability and one which make use of BLS signatures. But
these approaches were not privacy-preserving. POR/PDP
schemes evolved around an untrusted storage that presents a
publicly accessible remote interface to verify the tremendous
amount of data. Then comes the TPA based approach to keep
online storage honest. This scheme only works for encrypted
files which requires the auditor to keep state, and suffers from
bounded usage, which potentially brings in online burden to
users when the keyed hashes are used up. Schwarz et.al (2010)
proposes the use of remote data possession checking to
support unlimited times of file integrity verifications on data
stored across multiple distributed servers. This approach was
based on erasure-correcting code and efficient algebraic
signatures.
Some other works (Li et al., 2006; Ma et al., 2005; Xie et al.,
2007; Yavuz and Ning, 2009) considered the problem of
auditing the integrity for outsourced data. By openly
assuming an order of the records in database, Pang et al. (Ma
et al., 2005) used an aggregated signature to sign each record
with the information from two neighbouring records in the
ordered sequence, which ensures the result of a simple
selection query is continuous by checking the aggregated
signature. Li et al., 2006; Xie et al., 2007 used a Merkle tree to
audit the completeness of query results, but in some extreme
cases, the overhead could be as high as processing these
queries locally, which can significantly undermine the
benefits of database outsourcing. Moreover, to ensure
freshness, an extra system is needed to deliver the up-to-date
root signature to all clients in a reliable and timely manner
[11].
Wang et al. [13] make use of the Merkle Hash Tree to create
a public auditing mechanism with fully dynamic data. Zhu et
al. [15] used index hash tables to support fully dynamic data.
Wang et al. in [14] pondered data privacy with public auditing
in the cloud. In their mechanism, the TPA is able to check the
integrity of cloud data but cannot gain any private data. Zhu et
al. in [11] also aimed at a mechanism to preserve data privacy
from the TPA.
III. PROPOSED CONSTRUCTION
This section presents our privacy-preserving interactive
audit protocol for outsourced data in cloud storage. Firstly,
we will explain the security requirement of cloud audit
systems followed by the overview of interactive audit
architecture for outsourced data in cloud storage.
A. Security Requirements
The security of the proposed scheme can be stated using a
“game” that captures the data possession property [9], [10].
The data possession game between an adversary A who might
be a malicious CSP and a verifier V consists of the following:
Initialization: Verifier runs the KeyGen algorithm to generate
a key pair (PK, SK), and sends PK to Adversary.
Audit: Adversary interacts with Verifier to get the file and the
verification tags set τ. Verifier runs the algorithm TagGEN to
create the tags set τ, and returns to Adversary. Moreover,
Adversary can request challenge set {chal} and return proof
{χ} to Verifier. Verifier runs the Verify algorithm and
provides the verification results to Adversary. The Audit step
between Adversary and Verifier can be repeated polynomial
many times.
Challenge. Adversary decides on a file F previously used
during the Audit step, requests a challenge chal from Verifier,
and generates a proof. Upon receiving the proof χ, Verifier
runs the Verify algorithm and if Verify (PK, χ) fails, then
Adversary has won the game. The Challenge step can be
repeated polynomial-many times for the purpose of data
extraction. The proposed scheme is secure if the probability
that any probabilistic polynomial-time (PPT) adversary A
wins the game is negligible.
B. Design Goals
To allow privacy–preserving public auditing for cloud data
storage under this architecture, our protocol design should
attain the following security and performance assurances:
 Public Auditability: to allow TPA (or other clients
with the help of TPA) to verify the correctness of
cloud data on demand without retrieving a copy of
whole data or introducing additional on-line burden
to the cloud users;
 Verification-correctness: to ensure there exists no
cheating CSP that can pass the audit from TPA
without indeed storing users’ data intact;
 Privacy-preserving: to ensure that there exists no way
for TPA to derive users’ data from the information
collected during the auditing process; and
 Lightweight: to allow TPA to perform auditing with
minimum overheads in storage, communication and
computation, and to support statistical audit
sampling and optimized audit schedule with a long
enough period of time.

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C. Audit System Architecture for Cloud Storage
Audit system architecture for outsourced data in clouds is
shown in figure 1. This architecture can have four entities as
follows:
Data Owner (DO): who has large amount of data to be
outsourced to cloud
Cloud Service Provider (CSP): who offers data storage
service and has enough storage spaces and computation
resources
Third Party Auditor (TPA): who has abilities to manage or
monitor outsourced data under the entrustment of data owner.
Accorded Applications (AA): who have the right to access and
may work on stored data. These applications can be either
inside clouds or outside clouds according to the specific
necessities.
Fig. 1 Audit System Architecture for Cloud Storage
D. Notations And Preliminaries
We would like to introduce the notations and preliminaries.
 F – denotes the data file to be outsourced. It is the
sequence of b blocks m1, m2,….mb ɛ Zp
 H(.), h(.) denotes the cryptographic hash function
 Bilinear Map/Pairing: Let G1, G2 and GT be cyclic
groups of prime order p. Let g1 and g2 be generators
of G1 and G2, respectively. A bilinear pairing is a
map e : G1  G2  GT with bilinear,
non-degenerate, and computable properties.
E. Privacy Preserving Interactive Verification Correctness
Scheme
This section presents our proposed privacy preserving
verification correctness scheme. The protocol involves the
initialization phase, audit phase and verification phase.
The initialization phase uses two algorithms: key generation
and tag generation. In the key generation algorithm, each
client is assigned a secret key SK, which can be used to
generate the tags for the file to be outsourced, and a public key
PK, which is used to verify the integrity of those stored files.
This public auditing protocol provides the complete
outsourcing solution of data stored in cloud, as well provides
the data integrity of the data.
In tag generation algorithm, a public verification parameter
ω will be generated for each pre-processed file which consists
of a set of two values, t and η. The t is the set of tags for each
block along with the hash value i.e.; t = (δ (1)
, t1, t2, …..ts),
where hash value δ (1)
= H (FN). η = {ηi} i ɛ [1, b] is the hash
index table. The hash index table will take up the design based
upon the applications we use. As Zhu describes in [11], for a
Fig. 2 Interactive Verification Correctness Protocol
Fig 3 Initialization Phase
Initialization phase:
KeyGEN (1 κ
): Let Mg = (p, G, GT, e) be a system of
bilinear map group.
i. Randomly select two generators g1 , g2  G,
where G, GT are two group of large prime order p.
ii. Chooses a random sk1 , sk2  Zp.
iii. Computes H1 = g sk
2
1
and H 2 = g sk
2
2
 G.
iv. Thus, the secret key, SK = { sk1 , sk2 } and
v. public key is PK = { g1 , g2 , H1 , H 2 }
TagGEN (SK, F):
i. Split the file into b blocks which is again has s
sectors.
ii. Choose s random secret for this file r1, r2 ..... rs
 Zp
iii. Computes ti = g ri
1  G for i  [1,s]
iv. Compute hash value δ (1)
= H  (FN),
where δ = 

s
i
ri
1
and FN is the filename.
v. Construct hash index table η ={ ηi}i  [1,b]
vi. Calculate its tag as
 i 
(δi
(2)
) sk1
. g
s
j
skjmirj 

1
, 2
,
where δi
(2)
= H δ
(1)
( ηi) and i  [1, b]
vii. Set t = (δ (1)
, t1, t2, …..ts)
viii. Send outputs Τ = ( r1, r2 ..... rs ), ω = (t, η) to TPA
and τ = ( 1…. s ) to CSP

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954
static archival file, we can define ηi = Si, where Si is the
sequence number of block. For a dynamic file, we can define
as ηi = (Si || Ui || Ri), where Si is the sequence number of block,
Ui is the version number of updates for this block, and to
avoid collision we use a random number Ri. In order to
ensure the security of stored files the hash index table η is
considered very important.
In audit phase, the TPA first retrieves the file tag τ. Now it
comes to the “core” part of the auditing process. To generate
the challenge message for the audit chal, the TPA picks a
random c-element subset I = {C1,C2 . . . , Cc} of set [1, b]. For
each element i ∈ I, the TPA also chooses a random value νi (of
bit length that can be shorter than |p|, as in Wang et.al
(2011)). The message “chal” specifies the positions of the
blocks required to be checked. The TPA sends chal =
{(i,i )} i I to the server.
Fig 4: Audit Phase
Upon receiving challenge chal = {(i, i )} i I, the server
runs Response_Proof to generate a proof of data storage
correctness. The linear combination of sampled blocks,
chal = {i,i } i  I. Specifically, the server chooses a
random element ρ ← Zp, and calculates ψ ← e (
H
s
k
t j
2,
1



). To blind chal with ρ, the server computes ϰj ←
φj + ρ.  chali jimii),( ,  . Meanwhile, the server also
calculates an aggregated authenticator τ'
← iichalii

  .
),(  . It then sends ς = {τ', ϰ} as the response
proof of storage correctness to the TPA. The figure 4 shows
the audit phase.
Audit phase uses the proof generation protocol that has three
operations from 5 to 7 as shown in figure 2. This protocol is
similar to Schnorr’s Σ protocol, which is a zero-knowledge
proof system. By using this property, we can ensure that the
verification process does not disclose anything. To avoid the
leakage of stored data and tags in the verification process, the
private data { m ji, } are secured by a random  j  ℤ𝑝 and
the tags { i } are randomized by a ρ ∈ ℤ𝑝. Furthermore, the
values { j } and ρ are protected by the simple methods, i.e.,
H 
1
and ti j  𝔾, to prevent the adversaries from gaining
those properties. Moreover, it is obvious that this construction
admits a short constant-size response ς = (τ′, ϰ) ∈ 𝔾 × Z s
p
without being dependent on the size of challenge. That is
extremely important for large-size files.
Fig. 5 Verification Phase
Audit Phase: This phase uses the integrity-verification
protocol that perform the following operations
Challenge (CSP TPA): This is the core part of audit
algorithm. The chal message is used to specify the
positions of blocks need to be checked.
i. The verifier chooses a random challenge chal,
which has c-element subset I= {C1, C2, …..Cc} of
set [1, b].
ii. From this challenge set I, choose z indexes along
with z random coefficients υi ɛ Zp
iii. Then chal = {(i, υi )}i ɛ I be the set of challenge
index coefficient pairs
iv. TPA sends this chal to CSP.
Response_Proof (TPA CSP): CSP performs the
following operations
i. CSP chooses a random ρ  Zp and
ii. Chooses s random j R Zp for j ɛ [1,s]
iii. Find '
1H =

1
H
iv. Find ψ ← e ( H
s
k
jt 2,
1



)
v. CSP calculates ' ← iichalii

  
 ),(
vi. Calculates ϰj ← j + ρ.  chalii jimi),( , 
vii. ϰ = {ϰj} ],1[ sj
viii. Prover sends the response χ = (H'1, ψ ) and ς =
(τ', ϰ) to verifier
Verification Phase
Verify Response: The verifier can check that the
response_proof received are formed correctly by checking
the following
ψ. e (τ', g2) ? e (  chalvi H
v
ji
i
),(
'
1,)
)2(
( ) .
e( 

s
j
Hjt
j
1
2,

) –(1)
Identify Corrupted Data:
The verifier first validates P using equation (1).
i. The verifier asks the CSP to send ' = { i } bi 1 ,
where i = '   

chalvi ii
i
),(
 , hence
the verifier has two lists τList and ϰList
ii. Use a recursive divide and conquer approach, that the
verifier can identify the indices of corrupted data.
iii. The τList and ϰList are divided into two halves:
τList  (τLList: τRList) and ϰList  (ϰLList:
ϰRList).
iv. The verify equation (1) is recursively practiced on
τLList with ϰLList and τRList with ϰRList
Thus identifies the corrupted data in the outsourced data
and notifies it to the data owner.

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The verification phase uses the verification correctness
protocol that makes use of verify response operation to check
that the response_ proof retrieved is correct or not to generate
the audit result, AR to be 0 or 1. If the result retrieved to be
found 0 then it will check for the corrupted data. It is shown in
figure 5, how the corrupted data is identified.
F. Support for Dynamic Operations
As cloud services seem to provide scalability, so we need
to support dynamic operations. A dynamic operation includes
an insert, update or deletes operations on a single block.
While supporting dynamic operations it is essential that the
TPA to work with hash-index table η in order to follow the
current status of the stored files. This hash-index table has
four columns. The first column denotes the actual number i of
block bi, Si denotes the sequence number of the block, Vi de
notes the version number that indicates the number after
updating, and finally the Ri that denotes the random number
that prevents from collision.
Fig. 6 Dynamic Operations
G. Periodic Sampling Auditing and optimization of
parameters
If the audit is performed so frequently it would lead to excess
use of network bandwidth and computing resources of TPA,
Users, and CSPs. At the same time, too loose audits are not
approving to detect deception occurred in outsourced data.
Thus, to balance the overhead and reduce the malicious
attacks, it is necessary to diffuse the audit tasks during the
entire audit cycle. Hence to increase the audit efficiency and
to diminish the workload on servers, a sampling-based audit is
used. Assume that T is the audit period required to audit the
file, which is determined by how important it is for the data
owner. The audit period may be usually being assigned as 1
week or 1 month, and for important files may be set as 1 day.
Sampling based auditing is used to reduce the server
workload and also to increase the efficiency of auditing. Let
us assume that T is the audit period of the file and f is the
frequency at which the audit occurs, then the detection
probability can be found out as,
PT = 1 – (1- pb)
b.w.T.f
We take up that TPA modifies q blocks out of b-block file.
Then probability of disrupted blocks is pb = q/b. Let the ratio
of queried blocks in the total file blocks w = t/b under
different detection probability P = 1 - (1 - pb )
t
, where t be the
number of queried blocks for a challenge in the
response_proof.
For a file F with size fs = b. s sectors and probability p of
sector corruption, the detection probability of verify response
is P > 1 – ( 1- p)
fs. w
, where w= t/b denotes the sampling
probability in verify response.
IV EXPERIMENTAL RESULTS
The file F is used in our experimental results. Storage
auditing is a very resource demanding service in terms of
computational resource, communication cost and storage
space.
Firstly, we compute the performance of our scheme under
different parameters, such as file size, sampling ratio, and
number sectors per block. The stored files were chosen from
10 KB to 20 MB, the sector numbers were changed from 100
to 500 in terms of the file sizes, and the sampling ratios were
also changed from 10% to 50%. The Fig. 7a shows the
experimental results which prove that the computation and
communication costs rise with increase of file size and
sampling ratio.
Then, we relate the performance of initialization phase, for
each activity in audit phase and verification phase. We display
the experiment results in Fig. 7b, in which the computation
and communication costs of the “response_proof” and “verify
response” grow with the rise of sampling ratio whereas the
core part of protocol “challenge” is slightly changed for
sampling ratio.
Update(SK, ω, m'i ) : modifies the version number by Vi
 maxSi = Sj {Vj} + 1 and chooses a new Ri in ηi ɛ η to get
a new η'i; computes the new hash δi
(2)
= H δ
(1)
( “Si || Vi ||
Ri”); by using SK, computes
i' =
2
,
1
1
))2((
sk
ms
j
jtsk
j
ji













where t= {tj} ɛ ω, finally outputs O = (η'i, τ'i, m'i ).
Delete ( SK, ω, mi): computes the original τ' by mi and
computes the new hash δi
(2)
= H δ
(1)
( “Si || 0 || Ri”) and τ'i =
(δi
(2)
)sk1
; deletes i-th record to get a new ω'; finally outputs
O = (η'i, τi, τ'i )
Insert(SK, ω, m'i): inserts a new record in 𝑖-th position of
the index-hash table η ɛ ω and the other records move
backward in order; modifies Si  Si -1 , Vi  max Si = Sj
{Vj}and a random Ri in ηi ɛ η to get a new η'i ; computes
the new hash δi
(2)
= H δ
(1)
( “Si || Vi || Ri”) and τ'i = (δi
(2)
)sk1
.
m'i,j
) sk2
, where t= {tj} ɛ ω, finally outputs O = (η'i,
τ'i, m'i ).
Verify (U/D/I,O): The application sends the operation type
𝑈/𝐷/𝐼 and the result 𝑂 is given to 𝐶𝑆𝑃 via a secure
channel.
 For update or insert operations , CSP must check
whether the following
is held: for (η'i, τ'i, m'i ), e (τ', g2) ? e(δi
(2)
), H1)
.e( mi,j
, H2); and
 For Delete operation, 𝐶𝑆𝑃 must check whether τi is
equal to the stored τi and
e (τ', g2) ? e (H δ
(1)
( “Si || 0 || Ri”), H1).
In addition, TPA must replace ηi with the new η'i and check
the completeness η'i ɛ ω.

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956
Fig 7 Experimental results under different file size,sampling ratio, and sector number
V CONCLUSIONS
In this paper, we propose a scalable privacy-preserving
public auditing system for data storage security in Cloud
Computing. We utilize the interactive verification correctness
scheme to guarantee that the TPA would not learn any
knowledge about the data content stored on the cloud server
throughout the efficient auditing process, which not only
eliminates the burden of cloud user from the tedious and
possibly expensive auditing task, but also relieves the users’
fear of their outsourced data leakage. Our scheme uses the
fragment structure that has block which has sectors where
each block corresponds to a tag, so that the storage of
signature tags can be reduced by the increase of sectors; a
verifier can verify the integrity of file in random sampling
approach, which is of utmost importance for large files; these
schemes rely on homomorphic properties to aggregate data
and tags into a constant-size response, which minimizes the
overhead of network communication; and also identifies the
corrupted data. Thus our experimental results show that our
scheme is provably secure and efficient. This will help the
cloud servers which are expected to strongly cope with very
large scale data and thus inspire users to adopt cloud storage
services more confidently.
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Neethu Mariam Joseph has received the B.Tech degree in Information
Technology from Anna University of Technology, Coimbatore. She is
currently pursuing her M.Tech degree in the Network and Internet
Engineering at Karunya University, Coimbatore. Her research interests
include Computer Networks, Network Security and Cloud Computing.
Esther Daniel is working as Assistant .Professor in the Department of
Information Technology in Karunya University. She has done her Bachelor
of Engineering from Bharathiar University and Master of Engineering from
Karunya University. Her teaching experience spans to 7years. Her research
area includes Network Security, Cloud Computing etc. She has 6
publications at national and International level. She is doing part time
research in Information Technology at Anna University of Technology,
Coimbatore.
Dr. N.A.Vasanthi is currently the Professor and Head of Department of
Computer Science at Nehru Institute of Engineering and Technology. She
received the B.E degree in ICE and M.E. degree in CSE, both from
Bharathiar University, Coimbatore, India and Ph.D from Anna University.
She is a life member of CSI and ISTE. She has over two decades of teaching
and research experience. Her area of interest is Wireless Networks, Pervasive
Computing and Soft Computing. She has published papers in International
and National Journals.

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