Ieeepro techno solutions 2014 ieee dotnet project - distributed, concurrent, and independent

Distributed, Concurrent, and Independent
Access to Encrypted Cloud Databases
Luca Ferretti, Michele Colajanni, and Mirco Marchetti
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 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.
Index Terms—Cloud, security, confidentiality, SecureDBaaS, database
Ç
1 INTRODUCTION
IN a cloud context, where critical information is placed in
infrastructures of untrusted third parties, ensuring data
confidentiality is of paramount importance [1], [2]. This
requirement imposes clear data management choices:
original plain data must be accessible only by trusted
parties that do not include cloud providers, intermediaries,
and Internet; in any untrusted context, data must be
encrypted. Satisfying these goals has different levels of
complexity depending on the type of cloud service. There
are several solutions ensuring confidentiality for the storage
as a service paradigm (e.g., [3], [4], [5]), while guaranteeing
confidentiality in the database as a service (DBaaS) paradigm
[6] is still an open research area. In this context, we propose
SecureDBaaS as the first solution that allows cloud tenants
to take full advantage of DBaaS qualities, such as
availability, reliability, and elastic scalability, without
exposing unencrypted data to the cloud provider.
The architecture design was motivated by a threefold
goal: to allow multiple, independent, and geographically
distributed clients to execute concurrent operations on
encrypted data, including SQL statements that modify the
database structure; to preserve data confidentiality and
consistency at the client and cloud level; to eliminate any
intermediate server between the cloud client and the cloud
provider. The possibility of combining availability, elasti-
city, and scalability of a typical cloud DBaaS with data
confidentiality is demonstrated through a prototype of
SecureDBaaS that supports the execution of concurrent
and independent operations to the remote encrypted
database from many geographically distributed clients as
in any unencrypted DBaaS setup. To achieve these goals,
SecureDBaaS integrates existing cryptographic schemes,
isolation mechanisms, and novel strategies for management
of encrypted metadata on the untrusted cloud database. This
paper contains a theoretical discussion about solutions for
data consistency issues due to concurrent and independent
client accesses to encrypted data. In this context, we cannot
apply fully homomorphic encryption schemes [7] because of
their excessive computational complexity.
The SecureDBaaS architecture is tailored to cloud
platforms and does not introduce any intermediary proxy
or broker server between the client and the cloud
provider. Eliminating any trusted intermediate server
allows SecureDBaaS to achieve the same availability,
reliability, and elasticity levels of a cloud DBaaS. Other
proposals (e.g., [8], [9], [10], [11]) based on intermediate
server(s) were considered impracticable for a cloud-based
solution because any proxy represents a single point of
failure and a system bottleneck that limits the main
benefits (e.g., scalability, availability, and elasticity) of a
database service deployed on a cloud platform. Unlike
SecureDBaaS, architectures relying on a trusted intermedi-
ate proxy do not support the most typical cloud scenario
where geographically dispersed clients can concurrently
issue read/write operations and data structure modifica-
tions to a cloud database.
A large set of experiments based on real cloud platforms
demonstrate that SecureDBaaS is immediately applicable to
any DBMS because it requires no modification to the cloud
database services. Other studies where the proposed
architecture is subject to the TPC-C standard benchmark
for different numbers of clients and network latencies
show that the performance of concurrent read and write
operations not modifying the SecureDBaaS database
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, VOL. 25, NO. 2, FEBRUARY 2014 437
. The authors are with the University of Modena and Reggio Emilia, via
Vignolese 905/b, Modena 41125, Italy. E-mail: {luca.ferretti, michele.
colajanni, mirco.marchetti}@unimore.it.
Manuscript received 16 Sept. 2012; revised 14 Apr. 2013; accepted 23 May
2013; published online 30 May 2013.
Recommended for acceptance by X. Li, P. McDaniel, R. Poovendran, and
G. Wang.
For information on obtaining reprints of this article, please send e-mail to:
tpds@computer.org, and reference IEEECS Log Number TPDS-2012-09-0921.
Digital Object Identifier no. 10.1109/TPDS.2013.154.
1045-9219/14/$31.00 ß 2014 IEEE Published by the IEEE Computer Society

structure is comparable to that of unencrypted cloud
database. Workloads including modifications to the data-
base structure are also supported by SecureDBaaS, but at
the price of overheads that seem acceptable to achieve the
desired level of data confidentiality. The motivation of
these results is that network latencies, which are typical
of cloud scenarios, tend to mask the performance costs of
data encryption on response time. The overall conclusions
of this paper are important because for the first time they
demonstrate the applicability of encryption to cloud
database services in terms of feasibility and performance.
The remaining part of this paper is structured as follows:
Section 2 compares our proposal to existing solutions
related to confidentiality in cloud database services.
Sections 3 and 4 describe the overall architecture and how
it supports its main operations, respectively. Section 5
reports some experimental evaluation achieved through the
implemented prototype. Section 6 outlines the main results.
Space limitation requires us to postpone the assumed
security model in Appendix A, which can be found on
the Computer Society Digital Library at http://doi.
ieeecomputersociety.org/10.1109/TPDS.2013.154, to de-
scribe our solutions to concurrency and data consistency
problems in Appendix B, available in the online supple-
mental material, to detail the prototype architecture in
Appendix C, available in the online supplemental material.
2 RELATED WORK
SecureDBaaS provides several original features that differ-
entiate it from previous work in the field of security for
remote database services.
. It guarantees data confidentiality by allowing a
cloud database server to execute concurrent SQL
operations (not only read/write, but also modifica-
tions to the database structure) over encrypted data.
. It provides the same availability, elasticity, and
scalability of the original cloud DBaaS because it
does not require any intermediate server. Response
times are affected by cryptographic overheads that
for most SQL operations are masked by network
latencies.
. Multiple clients, possibly geographically distributed,
can access concurrently and independently a cloud
database service.
. It does not require a trusted broker or a trusted
proxy because tenant data and metadata stored by
the cloud database are always encrypted.
. It is compatible with the most popular relational
database servers, and it is applicable to different
DBMS implementations because all adopted solu-
tions are database agnostic.
Cryptographic file systems and secure storage solutions
represent the earliest works in this field. We do not detail
the several papers and products (e.g., Sporc [3], Sundr [4],
Depot [5]) because they do not support computations on
encrypted data.
Different approaches guarantee some confidentiality
(e.g., [12], [13]) by distributing data among different
providers and by taking advantage of secret sharing [14].
In such a way, they prevent one cloud provider to read its
portion of data, but information can be reconstructed by
colluding cloud providers. A step forward is proposed in
[15], that makes it possible to execute range queries on data
and to be robust against collusive providers. SecureDBaaS
differs from these solutions as it does not require the use of
multiple cloud providers, and makes use of SQL-aware
encryption algorithms to support the execution of most
common SQL operations on encrypted data.
SecureDBaaS relates more closely to works using en-
cryption to protect data managed by untrusted databases.
In such a case, a main issue to address is that cryptographic
techniques cannot be naı¨vely applied to standard DBaaS
because DBMS can only execute SQL operations over
plaintext data.
Some DBMS engines offer the possibility of encrypting
data at the filesystem level through the so-called Transpar-
ent Data Encryption feature [16], [17]. This feature makes it
possible to build a trusted DBMS over untrusted storage.
However, the DBMS is trusted and decrypts data before
their use. Hence, this approach is not applicable to the
DBaaS context considered by SecureDBaas, because we
assume that the cloud provider is untrusted.
Other solutions, such as [18], allow the execution of
operations over encrypted data. These approaches preserve
data confidentiality in scenarios where the DBMS is not
trusted; however, they require a modified DBMS engine
and are not compatible with DBMS software (both
commercial and open source) used by cloud providers.
On the other hand, SecureDBaaS is compatible with
standard DBMS engines, and allows tenants to build secure
cloud databases by leveraging cloud DBaaS services already
available. For this reason, SecureDBaaS is more related to
[9] and [8] that preserve data confidentiality in untrusted
DBMSs through encryption techniques, allow the execution
of SQL operations over encrypted data, and are compatible
with common DBMS engines. However, the architecture of
these solutions is based on an intermediate and trusted
proxy that mediates any interaction between each client and
the untrusted DBMS server. The approach proposed in [9]
by the authors of the DBaaS model [6] works by encrypting
blocks of data instead of each data item. Whenever a data
item that belongs to a block is required, the trusted proxy
needs to retrieve the whole block, to decrypt it, and to filter
out unnecessary data that belong to the same block. As a
consequence, this design choice requires heavy modifica-
tions of the original SQL operations produced by each
client, thus causing significant overheads on both the DBMS
server and the trusted proxy. Other works [10], [11]
introduce optimization and generalization that extend the
subset of SQL operators supported by [9], but they share the
same proxy-based architecture and its intrinsic issues. On
the other hand, SecureDBaaS allows the execution of
operations over encrypted data through SQL-aware en-
cryption algorithms. This technique, initially proposed in
CryptDB [8], makes it possible to execute operations over
encrypted data that are similar to operations over plaintext
data. In many cases, the query plan executed by the DBMS
for encrypted and plaintext data is the same.
The reliance on a trusted proxy that characterize [9] and
[8] facilitates the implementation of a secure DBaaS, and is
438 IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, VOL. 25, NO. 2, FEBRUARY 2014

applicable to multitier web applications, which are their
main focus. However, it causes several drawbacks. Since
the proxy is trusted, its functions cannot be outsourced to
an untrusted cloud provider. Hence, the proxy is meant to
be implemented and managed by the cloud tenant.
Availability, scalability, and elasticity of the whole secure
DBaaS service are then bounded by availability, scalability,
and elasticity of the trusted proxy, that becomes a single
point of failure and a system bottleneck. Since high
availability, scalability, and elasticity are among the
foremost reasons that lead to the adoption of cloud
services, this limitation hinders the applicability of [9]
and [8] to the cloud database scenario. SecureDBaaS solves
this problem by letting clients connect directly to the cloud
DBaaS, without the need of any intermediate component
and without introducing new bottlenecks and single
points of failure.
A proxy-based architecture requiring that any client
operation should pass through one intermediate server is
not suitable to cloud-based scenarios, in which multiple
clients, typically distributed among different locations, need
concurrent access to data stored in the same DBMS. On the
other hand, SecureDBaaS supports distributed clients
issuing independent and concurrent SQL operations to the
same database and possibly to the same data. SecureDBaaS
extends our preliminary studies [19] showing that data
consistency can be guaranteed for some operations by
leveraging concurrency isolation mechanisms implemented
in DBMS engines, and identifying the minimum isolation
level required for those statements. Moreover, we now
consider theoretically and experimentally a complete set of
SQL operations represented by the TPC-C standard bench-
mark [20], in addition to multiple clients and different
client-cloud network latencies that were never evaluated in
the literature.
3 ARCHITECTURE DESIGN
SecureDBaaS is designed to allow multiple and indepen-
dent clients to connect directly to the untrusted cloud
DBaaS without any intermediate server. Fig. 1 describes the
overall architecture. We assume that a tenant organization
acquires a cloud database service from an untrusted DBaaS
provider. The tenant then deploys one or more machines
(Client 1 through N) and installs a SecureDBaaS client on
each of them. This client allows a user to connect to the
cloud DBaaS to administer it, to read and write data, and
even to create and modify the database tables after creation.
We assume the same security model that is commonly
adopted by the literature in this field (e.g., [8], [9]), where
tenant users are trusted, the network is untrusted, and the
cloud provider is honest-but-curious, that is, cloud service
operations are executed correctly, but tenant information
confidentiality is at risk. For these reasons, tenant data, data
structures, and metadata must be encrypted before exiting
from the client. A thorough presentation of the security
model adopted in this paper is in Appendix A, available in
the online supplemental material.
The information managed by SecureDBaaS includes
plaintext data, encrypted data, metadata, and encrypted metada-
ta. Plaintext data consist of information that a tenant wants
to store and process remotely in the cloud DBaaS. To
prevent an untrusted cloud provider from violating con-
fidentiality of tenant data stored in plain form, SecureDBaaS
adopts multiple cryptographic techniques to transform
plaintext data into encrypted tenant data and encrypted tenant
data structures because even the names of the tables and of
their columns must be encrypted. SecureDBaaS clients
produce also a set of metadata consisting of information
required to encrypt and decrypt data as well as other
administration information. Even metadata are encrypted
and stored in the cloud DBaaS.
SecureDBaaS moves away from existing architectures
that store just tenant data in the cloud database, and save
metadata in the client machine [9] or split metadata
between the cloud database and a trusted proxy [8]. When
considering scenarios where multiple clients can access the
same database concurrently, these previous solutions are
quite inefficient. For example, saving metadata on the
clients would require onerous mechanisms for metadata
synchronization, and the practical impossibility of allowing
multiple clients to access cloud database services indepen-
dently. Solutions based on a trusted proxy are more
feasible, but they introduce a system bottleneck that
reduces availability, elasticity, and scalability of cloud
database services.
SecureDBaaS proposes a different approach where all
data and metadata are stored in the cloud database.
SecureDBaaS clients can retrieve the necessary metadata
from the untrusted database through SQL statements, so
that multiple instances of the SecureDBaaS client can access
to the untrusted cloud database independently with the
guarantee of the same availability and scalability properties
of typical cloud DBaaS. Encryption strategies for tenant
data and innovative solutions for metadata management
and storage are described in the following two sections.
3.1 Data Management
We assume that tenant data are saved in a relational
database. We have to preserve the confidentiality of the
stored data and even of the database structure because table
and column names may yield information about saved data.
We distinguish the strategies for encrypting the database
structures and the tenant data.
Encrypted tenant data are stored through secure tables
into the cloud database. To allow transparent execution of
FERRETTI ET AL.: DISTRIBUTED, CONCURRENT, AND INDEPENDENT ACCESS TO ENCRYPTED CLOUD DATABASES 439
Fig. 1. SecureDBaaS architecture.

SQL statements, each plaintext table is transformed into a
secure table because the cloud database is untrusted. The
name of a secure table is generated by encrypting the name
of the corresponding plaintext table. Table names are
encrypted by means of the same encryption algorithm
and an encryption key that is known to all the SecureDBaaS
clients. Hence, the encrypted name can be computed from
the plaintext name. On the other hand, column names of
secure tables are randomly generated by SecureDBaaS;
hence, even if different plaintext tables have columns with
the same name, the names of the columns of the
corresponding secure tables are different. This design
choice improves confidentiality by preventing an adversar-
ial cloud database from guessing relations among different
secure tables through the identification of columns having
the same encrypted name.
SecureDBaaS allows tenants to leverage the computa-
tional power of untrusted cloud databases by making it
possible to execute SQL statements remotely and over
encrypted tenant data, although remote processing of
encrypted data is possible to the extent allowed by the
encryption policy. To this purpose, SecureDBaaS extends the
concept of data type, that is associated with each column of a
traditional database by introducing the secure type. By
choosing a secure type for each column of a secure table, a
tenant can define fine-grained encryption policies, thus
reaching the desired trade-off between data confidentiality
and remote processing ability. A secure type is composed of
three fields: data type, encryption type, and field confidentiality.
The combination of the encryption type and of the field
confidentiality parameters defines the encryption policy of the
associated column.
The data type represents the type of the plaintext data
(e.g., int, varchar). The encryption type identifies the
encryption algorithm that is used to cipher all the data of
a column. It is chosen among the algorithms supported by
the SecureDBaaS implementation. As in [8], SecureDBaaS
leverages several SQL-aware encryption algorithms that
allow the execution of statements over encrypted data. It is
important to observe that each algorithm supports only a
subset of SQL operators. These features are discussed in
Appendix C, available in the online supplemental material.
When SecureDBaaS creates an encrypted table, the data type
of each column of the encrypted table is determined by the
encryption algorithm used to encode tenant data. Two
encryption algorithms are defined compatible if they produce
encrypted data that require the same column data type.
As a default behavior, SecureDBaaS uses a different
encryption key for each column; hence, equal values stored
in different columns are transformed into different en-
crypted representations. This design choice guarantees the
highest confidentiality level, because it prevents an adver-
sarial cloud provider to identify data that are repeated in
different columns. However, to allow remote processing of
SQL statements over encrypted data, sometimes it is
required to encrypt different columns by means of the
same encryption key. Common examples are the join
queries and the foreign key constraint.
The field confidentiality parameter allows a tenant to
define explicitly which columns of which secure table
should share the same encryption key (if any). SecureDBaaS
offers three field confidentiality attributes:
. Column (COL) is the default confidentiality level that
should be used when SQL statements operate on one
column; the values of this column are encrypted
through a randomly generated encryption key that is
not used by any other column.
. Multicolumn (MCOL) should be used for columns
referenced by join operators, foreign keys, and other
operations involving two columns; the two columns
are encrypted through the same key.
. Database (DBC) is recommended when operations
involve multiple columns; in this instance, it is
convenient to use the special encryption key that is
generated and implicitly shared among all the
columns of the database characterized by the same
secure type.
The choice of the field confidentiality levels makes it
possible to execute SQL statements over encrypted data
while allowing a tenant to minimize key sharing.
3.2 Metadata Management
Metadata generated by SecureDBaaS contain all the
information that is necessary to manage SQL statements
over the encrypted database in a way transparent to the
user. Metadata management strategies represent an original
idea because SecureDBaaS is the first architecture storing all
metadata in the untrusted cloud database together with the
encrypted tenant data. SecureDBaaS uses two types of
metadata.
. Database metadata are related to the whole database.
There is only one instance of this metadata type for
each database.
. Table metadata are associated with one secure table.
Each table metadata contains all information that is
necessary to encrypt and decrypt data of the
associated secure table.
This design choice makes it possible to identify which
metadata type is required to execute any SQL statement so
that a SecureDBaaS client needs to fetch only the metadata
related to the secure table/s that is/are involved in the SQL
statement. Retrieval and management of database metadata
are necessary only if the SQL statement involves columns
having the field confidentiality policy equal to database. This
design choice minimizes the amount of metadata that each
SecureDBaaS client has to fetch from the untrusted cloud
database, thus reducing bandwidth consumption and
processing time. Moreover, it allows multiple clients to
access independently metadata related to different secure
tables, as we discuss in Section 4.3 and Appendix B,
available in the online supplemental material.
Database metadata contain the encryption keys that are
used for the secure types having the field confidentiality set
to database. A different encryption key is associated with all
the possible combinations of data type and encryption type.
Hence, the database metadata represent a keyring and do
not contain any information about tenant data.
The structure of a table metadata is represented in Fig. 2.
Table metadata contain the name of the related secure table

and the unencrypted name of the related plaintext table.
Moreover, table metadata include column metadata for each
column of the related secure table. Each column metadata
contain the following information.
. Plain name: the name of the corresponding column of
the plaintext table.
. Coded name: the name of the column of the secure
table. This is the only information that links a
column to the corresponding plaintext column
because column names of secure tables are randomly
generated.
. Secure type: the secure type of the column, as defined
in Section 3.1. This allows a SecureDBaaS client to be
informed about the data type and the encryption
policies associated with a column.
. Encryption key: the key used to encrypt and decrypt
all the data stored in the column.
SecureDBaaS stores metadata in the metadata storage table
that is located in the untrusted cloud as the database. This is
an original choice that augments flexibility, but opens two
novel issues in terms of efficient data retrieval and data
confidentiality. To allow SecureDBaaS clients to manipulate
metadata through SQL statements, we save database and
table metadata in a tabular form. Even metadata confidenti-
ality is guaranteed through encryption. The structure of the
metadata storage table is shown in Fig. 3. This table uses
one row for the database metadata, and one row for each
table metadata.
Database and table metadata are encrypted through the
same encryption key before being saved. This encryption
key is called a master key. Only trusted clients that already
know the master key can decrypt the metadata and acquire
information that is necessary to encrypt and decrypt tenant
data. Each metadata can be retrieved by clients through an
associated ID, which is the primary key of the metadata
storage table. This ID is computed by applying a Message
Authentication Code (MAC) function to the name of the
object (database or table) described by the corresponding
row. The use of a deterministic MAC function allows clients
to retrieve the metadata of a given table by knowing its
plaintext name.
This mechanism has the further benefit of allowing
clients to access each metadata independently, which is an
important feature in concurrent environments. In addition,
SecureDBaaS clients can use caching policies to reduce the
bandwidth overhead.
4 OPERATIONS
In this section, we outline the setup setting operations
carried out by a database administrator (DBA), and we
describe the execution of SQL operations on encrypted data
in two scenarios: a naı¨ve context characterized by a single
client, and realistic contexts where the database services are
accessed by concurrent clients.
4.1 Setup Phase
We describe how to initialize a SecureDBaaS architecture
from a cloud database service acquired by a tenant from a
cloud provider. We assume that the DBA creates the
metadata storage table that at the beginning contains just
the database metadata, and not the table metadata. The
DBA populates the database metadata through the
SecureDBaaS client by using randomly generated encryp-
tion keys for any combinations of data types and encryption
types, and stores them in the metadata storage table after
encryption through the master key. Then, the DBA
distributes the master key to the legitimate users. User
access control policies are administrated by the DBA
through some standard data control language as in any
unencrypted database.
In the following steps, the DBA creates the tables of the
encrypted database. It must consider the three field
confidentiality attributes (COL, MCOL, and DBC) intro-
duced at the end of the Section 3. Let us describe this phase
by referring to a simple but representative example shown
in Fig. 4, where we have three secure tables named ST1,
ST2, and ST3. Each table STi (i ¼ 1; 2; 3) includes an
encrypted table Ti that contains encrypted tenant data, and a
table metadata Mi. (Although, in reality, the names of the
columns of the secure tables are randomly generated; for
the sake of simplicity, this figure refers to them through
C1-CN.)
For example, if the database has to support a join
statement among the values of T1.C2 and T2.C1, the DBA
must use the MCOL field confidentiality for T2.C1 that
references T1.C2 (solid arrow). In such a way, SecureDBaaS
can retrieve the encryption key specified in the column
metadata of T1.C2 from the metadata table M1 and can use
the same key for T2.C1. The solid arrow from M2 to M1
denotes that they explicitly share the encryption algorithm
and the key.
When operations (e.g., algebraic, order comparison)
involve more than two columns, it is convenient to adopt
the DBC field confidentiality. This has a twofold advantage:
we can use the special encryption key that is generated and
implicitly shared among all the columns of the database
Fig. 2. Structure of table metadata.
Fig. 3. Organization of database metadata and table metadata in the
metadata storage table.

characterized by the same secure type; we limit possible
consistency issues in some scenarios characterized by
concurrent clients (see Appendix B, available in the online
supplemental material). For example, the columns T1.C3,
T2.C3, and T3.C1 in Fig. 4 share the same secure type. Hence,
they reference the database metadata, as represented by the
dashed line, and use the encryption key associated with
their data and encryption types. As they have the same data
and encryption types, T1.C3, T2.C3, and T3.C1 can use the
same encryption key even if no direct reference exists
between them. The database metadata already contain
the encryption key K associated with the data and the
encryption types of the three columns, because the
encryption keys for all combinations of data and encryption
types are created in the initialization phase. Hence, K is
used as the encryption key of the T1.C3, T2.C3, and T3.C1
columns and copied in M1, M2, and M3.
4.2 Sequential SQL Operations
We describe the SQL operations in SecureDBaaS by
considering an initial simple scenario in which we
assume that the cloud database is accessed by one client.
Our goal here is to highlight the main processing steps;
hence, we do not take into account performance optimi-
zations and concurrency issues that will be discussed in
Section 4.3 and Appendix B, available in the online
supplemental material.
The first connection of the client with the cloud DBaaS is
for authentication purposes. SecureDBaaS relies on stan-
dard authentication and authorization mechanisms pro-
vided by the original DBMS server. After the authentication,
a user interacts with the cloud database through the
SecureDBaaS client. SecureDBaaS analyzes the original
operation to identify which tables are involved and to
retrieve their metadata from the cloud database. The
metadata are decrypted through the master key and their
information is used to translate the original plain SQL into a
query that operates on the encrypted database.
Translated operations contain neither plaintext database
(table and column names) nor plaintext tenant data.
Nevertheless, they are valid SQL operations that the
SecureDBaaS client can issue to the cloud database.
Translated operations are then executed by the cloud
database over the encrypted tenant data. As there is a one-
to-one correspondence between plaintext tables and en-
crypted tables, it is possible to prevent a trusted database
user from accessing or modifying some tenant data by
granting limited privileges on some tables. User privileges
can be managed directly by the untrusted and encrypted
cloud database. The results of the translated query that
includes encrypted tenant data and metadata are received
by the SecureDBaaS client, decrypted, and delivered to the
user. The complexity of the translation process depends on
the type of SQL statement.
4.3 Concurrent SQL Operations
The support to concurrent execution of SQL statements
issued by multiple independent (and possibly geographi-
cally distributed) clients is one of the most important benefits
of SecureDBaaS with respect to state-of-the-art solutions.
Our architecture must guarantee consistency among en-
crypted tenant data and encrypted metadata because
corrupted or out-of-date metadata would prevent clients
from decoding encrypted tenant data resulting in permanent
data losses. A thorough analysis of the possible issues and
solutions related to concurrent SQL operations on encrypted
tenant data and metadata is contained in Appendix B,
available in the online supplemental material. Here, we
remark the importance of distinguishing two classes of
statements that are supported by SecureDBaaS: SQL opera-
tions not causing modifications to the database structure,
such as read, write, and update; operations involving
alterations of the database structure through creation,
removal, and modification of database tables (data definition
layer operators).
In scenarios characterized by a static database structure,
SecureDBaaS allows clients to issue concurrent SQL com-
mands to the encrypted cloud database without introducing
any new consistency issues with respect to unencrypted
databases. After metadata retrieval, a plaintext SQL com-
mand is translated into one SQL command operating on
encrypted tenant data. As metadata do not change, a client
can read them once and cache them for further uses, thus
improving performance.
SecureDBaaS is the first architecture that allows con-
current and consistent accesses even when there are
operations that can modify the database structure. In such
cases, we have to guarantee the consistency of data and
metadata through isolation levels, such as the snapshot
isolation [21], that we demonstrate can work for most
usage scenarios.
Fig. 4. Management of the encryption keys according to the field
confidentiality parameter.

5 EXPERIMENTAL RESULTS
We demonstrate the applicability of SecureDBaaS to
different cloud DBaaS solutions by implementing and
handling encrypted database operations on emulated and
real cloud infrastructures. The present version of the
SecureDBaaS prototype supports PostgreSQL, MySql, and
SQL Server relational databases. As a first result, we can
observe that porting SecureDBaaS to different DBMS
required minor changes related to the database connector,
and minimal modifications of the codebase. We refer to
Appendix C, available in the online supplemental material,
for an in-depth description of the prototype implementation.
Other tests are oriented to verify the functionality of
SecureDBaaS on different cloud database providers. Experi-
ments are carried out in Xeround [22], Postgres Plus Cloud
Database [23], Windows SQL Azure [24], and also on an IaaS
provider, such as Amazon EC2 [25], that requires a manual
setup of the database. The first group of cloud providers
offer ready-to-use solutions to tenants, but they do not allow
a full access to the database system. For example, Xeround
provides a standard MySql interface and proprietary APIs
that simplify scalability and availability of the cloud
database, but do not allow a direct access to the machine.
This prevents the installation of additional software, the use
of tools, and any customization. On the positive side,
SecureDBaaS using just standard SQL commands can
encrypt tenant data on any cloud database service. Some
advanced computation on encrypted data may require the
installation of custom libraries on the cloud infrastructure.
This is the case of Postgres Plus Cloud that provides SSH
access to enrich the database with additional functions.
The next set of experiments evaluate the performance
and the overheads of our prototype. We use the Emulab [26]
testbed that provides us a controlled environment with
several machines, ensuring repeatability of the experiments
for the variety of scenarios to consider in terms of workload
models, number of clients, and network latencies.
As the workload model for the database, we refer to the
TPC-C benchmark [20]. The DBMS server is PostgreSQL9.1
deployed on a quad-core Xeon having 12 GB of RAM.
Clients are connected to the server through a LAN, where
we can introduce arbitrary network latencies to emulate
WAN connections that are typical of cloud services.
The experiments evaluate the overhead of encryption,
compare the response times of plain versus encrypted
database operations, and analyze the impact of network
latency. We consider two TPC-C compliant databases with
10 warehouses that contain the same number of tuples:
plain tuples consist of 1,046 MB data, while SecureDBaaS
tuples have size equal to 2,615 MB because of encryption
overhead. Both databases use repeatable read (snapshot)
isolation level [27].
In the first set of experiments, we evaluate the
overhead introduced when one SecureDBaaS client exe-
cutes SQL operations on the encrypted database. Client
and database server are connected through a LAN where
no network latency is added.
To evaluate encryption costs, the client measures the
execution time of the 44 SQL commands of the TPC-C
benchmark. Encryption times are reported in the histogram
of the Fig. 5 that has a logarithmic Y -axis. TPC-C operations
are grouped on the basis of the class of transaction: Order
Status, Delivery, Stock Level, Payment, and New Order.
From this figure, we can appreciate that the encryption time
is below 0.1 ms for the majority of operations, and below
1 ms for almost all operations but two. The exceptions are
represented by two operations of the Stock Level and
Payment transactions where the encryption time is two
orders of magnitude higher. This high overhead is caused
by the use of the order preserving encryption that is
necessary for range queries [28] (see Appendix C, available
in the online supplemental material).
To evaluate the performance overhead of encrypted
SQL operations, we focus on the most frequently executed
SELECT, INSERT, UPDATE, and DELETE commands of
the TPC-C benchmark. In Figs. 6 and 7, we compare the
response times of SELECT and DELETE, and UPDATE and
INSERT operations, respectively. The Y -axis reports the
boxplots of the response times expressed in ms (at a
different scale), while the X-axis identifies the SQL
operations. In SELECT, DELETE, and UPDATE operations,
the response times of SecureDBaaS SQL commands are
almost doubled, while the INSERT operation is, as
expected, more critical from the computational point of
view and it achieves a tripled response time with respect to
the plain version. This higher overhead is motivated by the
fact that an INSERT command has to encrypt all columns
Fig. 5. Encryption times of TPC-C benchmark operations grouped by the
transaction class.
Fig. 6. Plain versus encrypted SELECT and DELETE operations.

of a tuple, while an UPDATE operation encrypts just one
or few values.
The second set of the experiments is oriented to evaluate
the impact of network latency and concurrency on the use
of a cloud database from geographically distant clients. To
this purpose, we emulate network latencies through the
traffic shaping utilities available in the Linux kernel by
introducing synthetic delays from 20 to 150 ms in the
client-server connection. These values are representative of
round-trip times in continental (in the range of 40-60 ms)
and intercontinental (in the range of 80-150 ms) connec-
tions [29], that are expected when a cloud-based solution is
deployed. Table 1 reports the response times of the most
frequent SQL operations in the plain and encrypted cases
for 20, 40, and 80 ms latencies. The last column of this table
also reports the absolute and percentage overhead intro-
duced by SecureDBaaS.
These experimental results demonstrate that the re-
sponse times of the SQL operations issued to a remote
database are dominated by network latencies even in well-
connected regions. Each response time is two orders of
magnitude higher than the corresponding time of a plain
SQL operation in a LAN environment. Thanks to this effect,
the overhead of SecureDBaaS for the most common SELECT
operation falls from 57 percent to 1.31 percent and to
0.26 percent in correspondence of network latencies equal to
20 ms and 80 ms, respectively.
The last set of experiments assess the performance of
SecureDBaaS in realistic cloud database scenarios, as well as
its ability to support multiple, distributed, and independent
clients. The testbed is similar to that described previously,
but now the runs are repeated by varying the number of
concurrent clients (from 1 to 40) and the network latencies
(from plain LAN to delays reaching 150 ms). All clients
execute concurrently the benchmark for 300 seconds. The
results in terms of throughput refer to three types of
database operations:
. Original TPC-C: the standard TPC-C benchmark;
. Plain-SecureDBaaS: SecureDBaaS that use plain en-
cryption, that is, all SecureDBaaS functions and data
structures with no encryption; it allows us to
evaluate the overhead of SecureDBaaS without the
cost of cryptographic operations;
. SecureDBaaS: SecureDBaaS referring to the highest
confidentiality level.
Fig. 8 shows the system throughput referring to 20 clients
issuing requests to SecureDBaaS as a function of the
network latency. The Y -axis reports the number of
committed transactions per minute during the entire
experiment. This figure shows two important results:
. if we exclude the cryptographic costs, SecureDBaaS
does not introduce significant overheads. This can be
appreciated by verifying that the throughput of
plain SecureDBaaS and original TPC-C overlies for
any realistic Internet delay (>20 ms);
. as expected, the number of transactions per minute
executed by SecureDBaaS is lower than those
referring to original TPC-C and plain-SecureDBaaS,
but the difference rapidly decreases as the network
latency increases to the extent that is almost nullified
in any network scenario that can be realistically
referred to a cloud database context.
Figs. 9 and 10 show the throughput for increasing
numbers of concurrent clients in contexts characterized by
Fig. 7. Plain versus encrypted UPDATE and INSERT operations.
TABLE 1
Response Times and Overheads of SQL Operations for
Different Network Latencies
Fig. 8. TPC-C performance (20 concurrent clients).

40 ms and 80 ms network latencies, respectively. These
measures are optimistic representations of continental and
intercontinental delays. The Y -axis represents the number
of committed TPC-C transactions per minute executed by
the clients. The trends of the SecureDBaaS lines are close
to those of the original TPC-C database, thus demonstrat-
ing that SecureDBaaS encrypted database does not affect
scalability with respect to the plain database. Even more
important, the network latencies tend to mask crypto-
graphic overheads for any number of clients. For example,
the overheads of SecureDBaaS with 40 concurrent clients
decreases from 20 percent in a 40-ms scenario to 13 percent
in a realistic scenario, where the client-server latency is
equal to 80 ms. This result is important because it
confirms that SecureDBaaS is a valid and practical solution
for guaranteeing data confidentiality in real cloud data-
base services.
6 CONCLUSIONS
We propose an innovative architecture that guarantees
confidentiality of data stored in public cloud databases.
Unlike state-of-the-art approaches, our solution does not
rely on an intermediate proxy that we consider a single
point of failure and a bottleneck limiting availability and
scalability of typical cloud database services. A large part
of the research includes solutions to support concurrent
SQL operations (including statements modifying the
database structure) on encrypted data issued by hetero-
genous and possibly geographically dispersed clients. The
proposed architecture does not require modifications to
the cloud database, and it is immediately applicable
to existing cloud DBaaS, such as the experimented
PostgreSQL Plus Cloud Database [23], Windows Azure
[24], and Xeround [22]. There are no theoretical and
practical limits to extend our solution to other platforms
and to include new encryption algorithms.
It is worth observing that experimental results based on
the TPC-C standard benchmark show that the performance
impact of data encryption on response time becomes
negligible because it is masked by network latencies that
are typical of cloud scenarios. In particular, concurrent
read and write operations that do not modify the structure
of the encrypted database cause negligible overhead.
Dynamic scenarios characterized by (possibly) concurrent
modifications of the database structure are supported, but
at the price of high computational costs. These perfor-
mance results open the space to future improvements that
we are investigating.
ACKNOWLEDGMENTS
The authors would like to thank Prof. Lorenzo Alvisi of the
University of Texas at Austin for his constructive comments
on preliminary versions of this paper.
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Fig. 9. TPC-C performance (latency equal to 40 ms). Fig. 10. TPC-C performance (latency equal to 80 ms).

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Luca Ferretti received the master’s degree in
computer engineering from the University of
Modena and Reggio Emilia, Italy in 2012. He is
working toward the PhD degree at the Interna-
tional Doctorate School in information and
communication technologies (ICT) of the Uni-
versity of Modena and Reggio Emilia, Italy. His
research interests include information security,
and cloud architectures and services. His home
page is http://weblab.ing.unimo.it/people/ferretti.
Michele Colajanni received the master’s de-
gree in computer science from the University of
Pisa, and the PhD degree in computer engineer-
ing from the University of Roma in 1992. He is a
full professor in computer engineering at the
University of Modena and Reggio Emilia since
2000. He manages the Interdepartment Re-
search Center on Security and Safety (CRIS),
and the master in “Information Security: Tech-
nology and Law.” His research interests include
security of large-scale systems, performance and prediction models,
web and cloud systems. His home page is http://weblab.ing.unimo.it/
people/colajanni.
Mirco Marchetti received the PhD degree in
information and communication technologies
(ICT) in 2009. He is a postdoc at the Inter-
department Center for Research on Security and
Safety (CRIS), University of Modena and Reggio
Emilia. His research interests include intrusion
detection, cloud security, and all aspects of
information security. His home page is http://
weblab.ing.unimo.it/people/marchetti.
. For more information on this or any other computing topic,
please visit our Digital Library at www.computer.org/publications/dlib.

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