International Journal of Engineering Trends and Technology (IJETT) – Volume 10 Number 9 - Apr 2014
An Empirical Model of Data Integrity in Multi Cloud
Data Storage
T.Ravi Kiran1, G.Roshini2, K.Swetha Harshini3, G.Aparna Gayathri4
Assistant Professor1,B.Tech Scholar2,3,4
Dept of CSE, VITS College of Engineering, Sontyam, Visakhapatnam, Andhra Pradesh
Abstract: In present cloud storage services integrity became
major problem. For these traditional researchers
implemented so many approaches, but still its facing major
problem of security and data owners storing data in multiple
clouds. We implemented the security of our scheme based on
zeroknowledge proof system and which can satisfy
completeness and knowledge soundness and zeroknowledge
properties. The performance optimization mechanisms for
our scheme and in particular present an efficient method for
selecting optimal parameter values to minimize the
computation costs of clients and storage service providers.
I. INTRODUCTION
In present days the concept of third-party data
warehousing and more generally the data outsourcing has
becomequite popular. Outsourcing of data essentially
means that the data owner that client moves its data to a
third-party provider that means trusted server which is
supposed to presumablyfor a fee faithfully store the data
and make it available to the owner and perhaps others on
demand.Appealing features of outsourcing include
reducedcosts from savings in storage, maintenance
andpersonnel as well as increased availability and
transparentup-keep of data.
A number of security-related research issues in
dataoutsourcing have been studied in the past decade.
Previous researches concentrated on data authentication
and integrity and how to efficiently and securely ensure
that theserver returns correct and complete results in
response to its clients and their queries. Later research
focusedon outsourcing encrypted data which is placing
even less trust in the server and associated difficult
problems mainlyhaving to do with efficient querying over
encrypted domain.
More recently, however, the problem of Provable
Data Possession (PDP) –is also sometimes referred to as
Proof of Data Retrieval (POR) has popped upin the
research literature. The central goal in PDP isto allow a
client to efficiently and frequently and securelyverify that a
server who purportedly stores client’s potentiallyvery large
amount of datais not cheating the client. In this situation
cheating means that the servermight delete some of the data
ISSN: 2231-5381
or it might not store alldata in fast storageplace it on CDs
or other tertiaryoff-line media. It is important to note that a
storage server might not be malicious instead and it might
be simplyunreliable and lose or inadvertently corrupt
hosteddata. An effective PDP technique must be equally
applicableto malicious and unreliable servers. The
problemis further complicated by the fact that the client
mightbe a small device withlimited CPU, battery power
and communication facilities. The need to minimize
bandwidth and localcomputation overhead for the client in
performing every verification.
Several trends are opening up the era of Cloud
Computing, which is an Internet-based development and
use of computer technology. The ever cheaper and more
powerful processors, together with the software as a service
computing architecture, are transforming data centres into
pools of computing service on a huge scale. Mean-while,
the increasing network bandwidth and reliable yet flexible
network connections make it even possible that clients can
now subscribe high quality services from data and software
that reside solely on remote data centres. Although
envisioned as a promising service platform for the Internet,
this new data storage paradigm in “Cloud” brings about
many challenging design issues which have profound
influence on the security and performance of the overall
system. One of the biggest concerns with cloud data
storage is that of data integrity verification at untrusted
servers.
For example, the storage service provider, which
experiences Byzantine failures occasionally, may decide to
hide the data errors from the clients for the benefit of their
own. What is more serious is that for saving money and
storage space the service provider might neglect to keep or
deliberately delete rarely accessed data files which belong
to an ordinary client. Consider the large size of the
outsourced electronic data and the client’s constrained
resource capability, the core of the problem can be
generalized as how can the client find an efficient way to
perform periodical integrity verifications without the local
copy of data files.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 10 Number 9 - Apr 2014
II. RELATED WORK
infrastructures to meet the needs of diverse partners and
customers.
The research has demonstrated scheme such as Interactive
Protocol for Mobile Networks (IPMN) which can offer
completely edge intelligence based mobility with faster
hand-off, low delay, and low jitter.
Not only can interactive proof systems solve
problems not believed to be in NP, but under assumptions
about the existence of one-way functions, a prover can
convince the verifier of the solution without ever giving the
verifier information about the solution. This is important
when the verifier cannot be trusted with the full solution.
At first it seems impossible that the verifier could be
convinced that there is a solution when the verifier has not
seen a certificate, but such proofs, known as zeroknowledge proofs are in fact believed to exist for all
problems in NP and are valuable in cryptography.
While the designers of IP considered
generalizations interactive proof systems, others considered
restrictions. A very useful interactive proof system is
PCP(f(n), g(n)), which is a restriction of MA where Arthur
can only use f(n) random bits and can only examine g(n)
bits of the proof certificate sent by Merlin (essentially
using random access).
There are a number of easy-to-prove results about
various PCP classes. PCP(0,poly), the class of polynomialtime machines with no randomness but access to a
certificate, is just NP. PCP(poly,0), the class of
polynomial-time machines with access to polynomially
many random bits is co-RP. first major result was that
PCP(log, log) = NP; put another way, if the verifier in the
NP protocol is constrained to choose only O(log n) bits of
the proof certificate to look at, this won't make any
difference as long as it has O(log n) random bits to use.
Furthermore, the PCP theorem asserts that the
number of proof accesses can be brought all the way down
to a constant. That is, NP = PCP(log, O(1)).They used this
valuable characterization of NP to prove that
approximation algorithms do not exist for the optimization
versions of certain NP-complete problems unless P = NP.
Such problems are now studied in the field known as
hardness of approximation.
Multi-cloud strategy is the concomitant use of two
or more cloud services to minimize the risk of widespread
data loss or downtime due to a localized component failure
in a cloud computing environment. Such a failure can occur
in hardware, software, or infrastructure. A multi-cloud
strategy can also improve overall enterprise performance
by avoiding "vendor lock-in" and using different
ISSN: 2231-5381
Reasons for an adverse cloud event can vary from
a single cable connector failure to an EMP
(electromagnetic pulse), or from a natural disaster to an act
of cyber-war-fare. Even the failure of a single hard
disk/drive unit can result in a large-scale network outage if
the malfunction takes place at a critical point in the system
such as a host computer.
As customer bases and device types grow
increasingly diverse (yet at the same time increasingly
specialized), organizations face a complex array of
challenges in their quest to satisfy the demands of all end
users. In particular, the speed with which a given Website
loads has a huge impact on customer satisfaction. Recent
research has revealed that the average user expects a
Webpage to load just as fast on a mobile device as it would
on their home computer (two seconds or less). Because
faster page loading results in more frequent and longer
visits to a given Website, page loading time can indirectly
affect rankings in search engines. A multi-cloud strategy
can help an organization to minimize page loading times
for all types of content.
A multi-cloud approach can offer not only the
hardware, software and infrastructure redundancy
necessary to optimize fault tolerance, but it can also steer
traffic from different customer bases or partners through
the fastest possible parts of the network. Some clouds are
better suited than others for a particular task. For example,
a certain cloud might handle large numbers of requests per
unit time requiring small data transfers on the average, but
a different cloud might perform better for smaller numbers
of requests per unit time involving large data transfers on
the average. Some organizations use a public cloud to make
resources available to consumers over the Internet and a
private cloud to provide hosted services to a limited
number of people behind a firewall. A third type of cloud,
called a hybrid cloud, may also be used to manage
miscellaneous internal and external services.
III. PROPOSED WORK
Zero Knowledge Proof method:
An archetypical \cryptographic" problem consists
of providing mutually distrustful parties with a means of
\exchanging" (predetermined) \pieces of information". The
setting consists of several parties, each wishing to obtain
some predetermined partial information concerning the
secrets of the other parties. Yet each party wishes to reveal
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International Journal of Engineering Trends and Technology (IJETT) – Volume 10 Number 9 - Apr 2014
as little information as possible about its own secret. To
clarify the issue, let us consider a specific example.
Suppose that all users in a system keep backups of
their entire system, encrypted using their public-key
encryption, in a publicly accessible storage media. Suppose
that at some point, one user, called Alice, wishes to reveal
to another user, called Bob, the clear text of one of her
files (which appears in one f her backups). A trivial
\solution" is for Alice just to send the (clear text) file to
Bob. The problem with this \solution" is that Bob has no
way of verifying that
Alice really sent him a file from
her public backup, rather than just sending him an arbitrary
le. Alice can simply prove that she sends the correct file
by revealing to Bob her private encryption key. However,
doing so, will reveal toBob the contents of all her files,
which is certainly something that Alice doesnot want to
happen. The question is whether Alice can convince Bob
that sheindeed revealed the correct le without yielding any
additional knowledge".
An analogous question can be phrased formally as
follows. Let f be a one-waypermutation, and b a hard-core
predicate with respect to f. Suppose that oneparty, A, has a
string x, whereas another party, denoted B, only has
f(x).Furthermore, suppose that A wishes to reveal b(x) to
party B, without yieldingany further information. The
trivial \solution" is to let A send b(x) to B, but,as explained
above, B will have no way of verifying whether A has
really sentthe correct bit (and not its complement). Party A
can indeed prove that it sendsthe correct bit (i.e., b(x)) by
sending x as well, but revealing x to B is muchmore than
what A had originally in mind. Again, the question is
whether A canconvince B that it indeed revealed the
correct bit (i.e., b(x)) without yieldingany additional
\knowledge".
In general, the question is whether it is possible to
prove a statement without yieldinganything beyond its
validity. Such proofs, whenever they exist, are called zeroknowledge,and play a central role (as we shall see in the
subsequent chapter) in the construction of\cryptographic"
protocols.
An archetypical \cryptographic" problem consists
of providing mutually distrustful parties with a means of
\exchanging" (predetermined) \pieces of information". The
setting consists of several parties, each wishing to obtain
some predetermined partial information concerning the
secrets of the other parties. Yet each party wishes to reveal
as little information as possible about its own secret. To
clarify the issue, let us consider a specific example.
ISSN: 2231-5381
Suppose that all users in a system keep backups of
their entire file system, encrypted using their public-key
encryption, in a publicly accessible storage media. Suppose
that at some point, one user, called Alice, wishes to reveal
to another user, called Bob, the clear text of one of her les
(which appears in one of her backups). A trivial \solution"
is for Alice just to send the (clear text) le to
Bob. The problem with this \solution" is that Bob
has no way of verifying that Alice really sent him a le
from her public backup, rather than just sending him an
arbitrary file. Alice can simply prove that she sends the
correct file by revealing to Bob her private encryption key.
However, doing so, will reveal to Bob the contents of all
her les, which is certainly something that Alice does not
want to happen. The question is whether Alice can
convince Bob that she indeed revealed the correct file
without yielding any additional \knowledge".
An analogous question can be phrased formally as
follows. Let f be a one-waypermutation, and b a hard-core
predicate with respect to f. Suppose that oneparty, A, has a
string x, whereas another party, denoted B, only has
f(x).Furthermore, suppose that A wishes to reveal b(x) to
party B, without yieldingany further information. The
trivial \solution" is to let A send b(x) to B, but,as explained
above, B will have no way of verifying whether A has
really sentthe correct bit (and not its complement). Party A
can indeed prove that it sendsthe correct bit (i.e., b(x)) by
sending x as well, but revealing x to B is muchmore than
what A had originally in mind. Again, the question is
whether A canconvince B that it indeed revealed the
correct bit (i.e., b(x)) without yieldingany additional
\knowledge".
In general, the question is whether it is possible to
prove a statement without yieldinganything beyond its
validity. Such proofs, whenever they exist, are called zeroknowledge,and play a central role in the construction
of\cryptographic" protocols.
Prover and Verifier
The notion of a prover is implicit in all
discussions of proofs, be it in mathematics or inreal-life
situations. Instead, the emphasis is placed on the
verification process, or in otherwords on (the role of ) the
verifier. Both in mathematics and in real-life situations,
proofsare defined in terms of the verification procedure.
Typically, the verification procedure isconsidered to be
relatively simple, and the burden is placed on the
party/person supplyingthe proof (i.e., the prover).
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International Journal of Engineering Trends and Technology (IJETT) – Volume 10 Number 9 - Apr 2014
Prover and VerifierThe notion of a prover is
implicit in all discussions of proofs, be it in mathematics or
inreal-life situations. Instead, the emphasis is placed on the
verification process, or in otherwords on (the role of ) the
verifier. Both in mathematics and in real-life situations,
proofsare defined in terms of the verification procedure.
Typically, the verification procedure isconsidered to be
relatively simple, and the burden is placed on the
party/person supplyingthe proof (i.e., the prover).
Completeness and Validity
Two fundamental properties of a proof system
(i.e., a verification procedure) are its validityand
completeness. The validity property asserts that the
verification procedure cannot be\tricked" into accepting
false statements. In other words, validity captures the
verifierability of protecting itself from being convinced of
false statements (no matter what theprover does in order to
fool it). On the other hand, completeness captures the
ability ofsome prover to convince the verifier of true
statements (belonging to some predeterminedset of true
statements). Note that both properties are essential to the
very notion of a proofsystem.
IV. CONCLUSION
In this paper, we presented the construction of an efficient
PDP scheme for distributed cloud storage. Based on
homomorphism verifiable response and hash index
hierarchy, we have proposed a cooperative PDP scheme to
support dynamic scalability on multiple storage servers.
We also showed that our scheme provided all security
properties required by zeroknowledge interactive proof
system, so that it can resist various attacks even if it is
deployed as a public audit service in clouds. Furthermore,
we optimized the probabilistic query and periodic
verification to improve the audit performance. Our
experiments clearly demonstrated that our approaches only
introduce a small amount of computation and
communication overheads. Therefore, our solution can be
treated as a new candidate for data integrity verification in
outsourcing data storage systems.
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BIOGRAPHIES
T.Ravi Kiran is an Assistant Professor in
the Department of Computer Science &
Engineering, VITS College of Engineering,
Sontyam, Visakhapatnam, Andhra Pradesh.
He has 5 years of experience in Teaching.
His research interests include Cloud
Computing, Web Technologies, Information Security, Data
Mining, Search Engines, Information Retrieval, Network
Security, Database Systems, Data Privacy, Image
Processing, Computer Networks.
G.Roshini is currently pursuing B.Tech.
degree
in
Computer
Science
&
Engineering, VITS College of Engineering,
Sontyam, Visakhapatnam, Andhra Pradesh.
Her research interests include Cloud
Computing, Information Security.
K.Swetha Harshini is currently pursuing
B.Tech. degree in Computer Science &
Engineering, VITS College of Engineering,
Sontyam, Visakhapatnam, Andhra Pradesh.
Her research interests include Cloud
Computing, Information Security.
REFRENCES
[1] B. Sotomayor, R. S. Montero, I. M. Llorente, and I. T. Foster,“Virtual
infrastructure management in private and hybridclouds,” IEEE Internet
Computing, vol. 13, no. 5, pp. 14–22,2009.
[2] G. Ateniese, R. C. Burns, R. Curtmola, J. Herring, L. Kissner,Z. N. J.
Peterson, and D. X. Song, “Provable data possessionat untrusted stores,”
in ACM Conference on Computer andCommunications Security, P. Ning,
S. D. C. di Vimercati, andP. F. Syverson, Eds. ACM, 2007, pp. 598–609.
[3] A. Juels and B. S. K. Jr., “Pors: proofs of retrievability forlarge files,”
in ACMConference on Computer and CommunicationsSecurity, P. Ning,
S. D. C. di Vimercati, and P. F. Syverson, Eds.ACM, 2007, pp. 584–597.
ISSN: 2231-5381
G.Aparna Gayathri is currently pursuing
B.Tech. degree in Computer Science &
Engineering,
VITS
College
of
Engineering, Sontyam, Visakhapatnam,
Andhra Pradesh. Her research interests
include Cloud Computing, Information
Security.
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