Excellent Manner of Using Secure way of data storage in cloud computing

Scientific Journal Impact Factor (SJIF): 1.711
International Journal of Modern Trends in Engineering
and Research
www.ijmter.com
@IJMTER-2014, All rights Reserved 65
e-ISSN: 2349-9745
p-ISSN: 2393-8161
Excellent Manner of Using Secure way of data storage in cloud
computing
Poorna Chandra Reddy Alla, G.Komala, J.Purna Prakash
1Assoc.Professor, 2Asst.Professor, 3Assoc.Professor
1,2,3Department of Computer Science & Engineering, Christu Jyoti Institute of Technology & Science
Jangon (Mdl), Warngal(Dist)-506167,India
Abstract— The major challenging issue in Cloud computing is Security. Providing Security is big issue
towards protecting data from third person as well as in Internet. This mainly deals the Security how it is
provided. Various type of services are there to protect our data and Various Services are available in Cloud
Computing to Utilize effective manner as Software as a Service (SaaS), Platform as a Service (PaaS),
Hardware as a Service (HaaS). Cloud computing is the use of computing resources (hardware and
software) that are delivered as a service over Internet network. Cloud Computing moves the Application
software and databases to the large data centres, where the administration of the data and services may not
be fully trustworthy that is in third party here the party has to get certified and authorized. Since Cloud
Computing share distributed resources via network in the open environment thus it makes new security
risks towards the correctness of the data in cloud. I propose in this paper flexibility of data storage
mechanism in the distributed environment by using the homomorphism token generation. In the proposed
system, users need to allow auditing the cloud storage with lightweight communication. While using
Encryption and Decryption methods it is very burden for a single processor. Than the processing
Capabilities can we utilize from Cloud Computing.
Keywords: Cloud computing, Processing Capabilities in SaaS distributed server, error localization and
data integrity
I. INTRODUCTION
The global presence of the Internet and the introduction of the wireless networking and mobile devices
that always feature in Internet connectivity have raised user expectations and their demands for services
from the Internet. However, the architectures required by service providers to enable Web 2.0 have created
an IT service that is differentiated by its resilience, scalability, reusability, interoperability, security, and
open platform development. This has effectively become the backbone of Cloud Computing and is
considered by a number of vendors and services to be an operating system layer of its own (CPNI, Centre
for the Protection of National Infrastructure). Cloud Computing appears as both a computational model or
paradigm and distribution architecture, and its principal objective is to provide secure, quick, convenient
data storage, and net computing services, with all computing resources being visualized as services and
delivered via the Internet. The Cloud enhances collaboration, agility, scaling, and availability, the ability to
scale to fluctuations according to demand and accelerate development work, and provides the potential for
cost reduction through optimized and efficient computing. As cloud computing is an Internet based
computer technology. Some of the major firms like Amazon, Microsoft and Google have implemented the
“CLOUD” and have been using it to speed up their business. Cloud computing has given a new dimension
to the complete outsourcing arena (SaaS, PaaS and IaaS) and they provide ever cheaper powerful
processor with these computing architecture.

International Journal of Modern Trends in Engineering and Research (IJMTER)
Volume 02, Issue 01, [January - 2015] e-ISSN: 2349-9745, p-ISSN: 2393-8161
In order to achieve the assurances of the cloud data integrity and availability and enforce the quality of
cloud storage service, efficient methods that enable on-demand data correctness verification on behalf of
cloud users. However ensuring storage correctness without having users possessing data, cannot address
all the security threats in cloud data storage, since they are all focusing on single server scenario and most
of them do not consider dynamic data operations like inserting, appending and deleting. This is conquered
using distributed protocols for ensuring storage correctness across multiple servers or peers. In this paper,
the propose an effective and flexible concept with explicit dynamic data support to ensure the correctness
of users data in the cloud and also rely on erasure- correcting code in the file distribution preparation to
provide redundancies and guarantee the data dependability. By using the homomorphic token generation
can achieve correctness of the data as well fast error localization. Error Localization is the data corruption
that has been detected during the storage correctness verification, our scheme can almost guarantee the
simultaneous localization of data errors, i.e., the identification of the misbehaving server(s). In this paper
the main contribution as follows:
• It compared too many of its predecessors providing only binary results about the data storage status
across the distributed server, the proposed designed to achieve the data error localization.
• Providing the secure and dynamic operation (i.e., insert, append, modify and delete) on data blocks.
• The security and performance analysis shows the proposed design is highly efficient and resilient
against Byzantine failure, malicious data modification attack, and even server colluding attacks.
The rest of the paper is organized as follows: Section II introduces the system model, and design goals,
Section III providing the detailed description of the scheme, Section IV provides dynamic data operation
support, Section V provides the security issue and performance analysis, and Section VI gives the
concluding of the paper.
II. PROBLEM STATEMENT
1. Third Party problem
In the network architecture for cloud storage service having three different network entities are
follows:
• User: User is an entity for using the services in cloud computing the data to be stored in the cloud
server and rely on the cloud for data computation.
• Cloud Server Provider: CSP is an entity, Various Service providers are there like IBM, Mifcosoft,
Amazon servers available now a dasys
• Third Party Auditor: TPA who has expertise and capabilities that user may not have trusted to
assess and expose the risk of the cloud storage service.

Fig. 1: Cloud storage service architecture
In the cloud data storage, a user stores his data through the cloud service provider into a set of the cloud
servers. The cloud servers run on the distributed system. Data redundancy can be applied with technique of
erasure correcting code to further tolerate faults or server crash as users grows in size. By using
application, the user interacts with cloud server via cloud service provider to access or retrieve his data. In
some cases, the user may need to perform block level operations on his data. The most general forms of
these operations are considering are block revise, erase, insert and affix.
As users no longer possess their data locally, it is of critical importance to assure users that their data are
being correctly stored and maintained. That is, users should be prepared with security means so that they
can make continuous correctness assurance of their data stored in Cloud Servers even without the
existence of local copies. In case those users do not necessarily have the time, feasibility or resources to
monitor their data, they can delegate the tasks to an optional trusted Third Party Auditor of their respective
choices. In our model, assume that the peer-to-peer communication channels between each cloud server
and the user is authenticated and reliable, which can be achieved in practice with little overhead.
2. Adversary Model
Security intimidation faced by data stored in cloud servers come from two different sources. One hand, a
CSP can be self-centred, un-trusted and probably malevolent. Not only it desires to move data that has not
been or is rarely accessed to a lower tier of storage than agreed for fiscal reasons, but it may also attempt
to hide a data loss incident due to management errors, Byzantine failures and so on. On the other hand,
there may also exist an economically- motivated antagonist, who has the capability to compromise a
number of cloud data storage servers in different time intervals and subsequently is able to modify or erase
users’ data while remaining undetected by CSPs for a certain period. Specifically, we consider two types
of Antagonist with different levels of capability:
Strong Adversary: This is the worst case scenario, in which we assume that the antagonist can
compromise all the storage servers so that he can intentionally modify the data files as long as they are
internally consistent. In fact, this is equivalent to the case where all servers are colluding together to hide a
data loss or corruption incident.
Weak Adversary: The antagonist is interested in corrupting the user’s data files stored on individual
servers. Once a server is comprised, an antagonist can pollute the original data files by modifying or
introducing its own fraudulent data to prevent the original data from being retrieved by the user.

3. Design Targets:
To make sure the security and dependability for data storage in cloud under the aforementioned
antagonist model, we aim to design efficient mechanisms for dynamic data verification and operation and
achieve the following goals:
I. Storage accuracy: to ensure users that their data are indeed stored appropriately and kept intact all
the time in the cloud.
II. Fast localization of data error: to effectively locate the malfunctioning server when data
corruption has been detected.
III. Dynamic data support: to maintain the same level of storage correctness assurance even if users
modify, erase or affix their data files in the cloud.
IV. Dependability: to enhance data availability against Byzantine failures, malicious data modification
and server colluding attacks, i.e. minimizing the effect brought by data errors or server failures.
V. Lightweight: to enable users to perform storage correctness checks with minimum overhead.
4. Notation and Preliminaries:
F – Data file to be stored. F is denoted as a matrix of m equal-sized data vectors, each consisting of
l blocks.
A – Scattering matrix used for coding.
G – Encoded file matrix, which includes a set of
n = m + k vectors, each consisting of l blocks.
f – Function , which is defined as f : {0, 1} × key
III. ENSURING CLOUD DATA STORAGE
In cloud data storage system, users store their data in the cloud and no longer possess the data
locally. Thus, the correctness and availability of the data files being stored on the distributed cloud servers
must be guaranteed. One of the key issues is to effectively detect any unauthorized data modification and
corruption, possibly due to server compromise and/or random Byzantine failures. Besides, in the
distributed case when such inconsistencies are successfully detected, to find which server the data error
lies in is also of great significance, since it can be the first step to fast recover the storage errors. To
address these problems, our main scheme for ensuring cloud data storage is presented in this section. The
first part of the section is devoted to a review of basic tools from coding theory that are needed in our
scheme for file distribution across cloud servers. Then, the homomorphic token is introduced. The token
computation function we are considering belongs to a family of universal hash function, chosen to
preserve the homomorphic properties, which can be perfectly integrated with the verification of erasure
coded data.
Subsequently, it is also shown how to derive a challenge response protocol for verifying the storage
correctness as well as identifying misbehaving servers. Finally, the procedure for file retrieval and error
recovery based on erasure-correcting code is outlined.
Token Pre-computation
In order to achieve assurance of data storage correctness and data error localization, our scheme
entirely relies on the pre-computed verification tokens. The main idea is before file distribution the user
pre-computes a certain number of short verification tokens on individual; each token covers a random
subset of data blocks. Later, when the user wants to make sure the storage correctness for the data in the
cloud, he challenges the cloud servers with a set of randomly generated block indices. After getting

assurance of the user it again asks for authentication by which the user is confirmed to be the authenticated
user. Upon receiving assurance, each cloud server computes a short “signature” over the specified blocks
and returns them to the user. The values of these signatures should match the corresponding tokens pre-
computed by the user. Meanwhile, as all servers operate over the same subset of the indices, the requested
response values for integrity check must also be a valid codeword determined by a secret matrix.
Suppose the user wants to challenge the cloud server’s t times to make sure the correctness of data
storage. Then, he must pre-compute t verification tokens for each function, a challenge key and a master
key are used. To generate the ith token for server j, the user acts as follows:
I. Derive a arbitrary value i and a permutation key based on master permutation key.
II. Compute the set of randomly-chosen indices:
III. Calculate the token using encoded file and the arbitrary value derived.
Algorithm 1 Token Pre-computation
1. Procedure
2. Choose parameters l, n and function f;
3. Choose the number t of tokens;
4. Choose the number r of indices per verification;
5. Generate master key and challenge key;
6. for vector G(j), j ←1, n do
7. for round i← 1, t do
8. Derive i = f(i) and k(i) from master key .
9. Compute v(j)
10. end for
11. end for
12. Store all the via locally.
13. end procedure
Correctness Verification and Error Localization
Error localization is a key requirement for eradicating errors in storage systems. However, many
previous schemes do not explicitly consider the problem of data error localization. Thus it only provides
binary results for the storage verification. Our scheme provides those by integrating the correctness
verification and error localization in our challenge-response protocol: the response values from servers for
each challenge not only determine the correctness of the distributed storage, but also contain information
to locate potential data error(s).
Specifically, the procedure of the ith challenge-response for a cross-check over the n servers is described
as follows:
I. The user reveals the i as well as the ith key k (i) to each servers
II. The server storing vector G aggregates those r rows
III. Specified by index k(i) into a linear combination R
IV. Upon receiving R is from all the servers, the user takes away values in R.
V. Then the user verifies whether the received values remain a valid codeword determined by
secret matrix.
Because all the servers operate over the same subset of indices, the linear aggregation of these r
specified rows (R(1)i , . . . ,R(n)i ) has to be a codeword in the encoded file matrix. If the above equation

holds, the challenge is passed. Otherwise, it indicates that among those specified rows, there exist file
block corruptions. Once the inconsistency among the storage has been successfully detected, we can rely
on the pre-computed verification tokens to further determine where the potential data error(s) lies in.
Note that each response R(j) i is computed exactly in the same way as token v(j) i , thus the user can
simply find which server is misbehaving by verifying the following n equations:
Algorithm 2 gives the details of correctness verification and error localization.
1. procedure CHALLENGE(i)
2. Recompute i = fl (i) and k(i) master key ;
3. Send {i, k(i) } to all the cloud servers;
4. Receive from servers R
5. for (j ← m + 1, n) do
6. R(j) ← R(j)−Prq=1 fkj (sIq,j)·_qi , Iq = _k(i)prp(q)
7. end for
8. if ((R(1)i , . . . ,R(m)i ) ·P==(R(m+1)i , . . .
,R(n)i )) then
9. Accept and ready for the next challenge.
10. else
11. for (j ← 1, n) do
12. if (R ! =V ) then
13. return server is misbehaving.
14. end if
15. end for
16. end if
17. end procedure
IV. PROVIDING DYNAMIC DATA OPERATION SUPPORT
So far, we assumed that F represents archived data. However, in cloud data storage, there are many
potential scenarios where data stored in the cloud is dynamic, like electronic documents, photos, or log
files etc. Therefore, it is crucial to consider the dynamic case, where a user may wish to perform various
block-level operations of revise, erase and affix to modify the data file while maintaining the storage
correctness assurance.The straightforward and insignificant way to support these operations is for user to
download all the data from the cloud servers andre-compute the whole parity blocks as well as verification
tokens. This would clearly be highly inefficient. In this section, we will show how our scheme can
unambiguously and efficiently handle dynamic data operations for cloud data storage.
Revise Operation In cloud data storage, sometimes the user may need to modify some data block(s)
stored in the cloud, from its current value f to a new one. We refer to this operation as data revise.
Sometimes Erase Operation, after being stored in the cloud, certain data blocks may need to be
erased. The erase operation we are considering is a general one, in which user replaces the data block with
zero or some special reserved data symbol. From this point of view, the erase operation is actually a
special case of the data revise operation, where the original data blocks can be replaced with zeros or some
predetermined special blocks.
In some cases Append Operation, the user may want to increase the size of his stored data by
adding blocks at the end of the data file, which we refer as data append. We anticipate that the most

frequent append operation in cloud data storage is bulk append, in which the user needs to upload a large
number of blocks (not a single block) at one time.
An affix operation to the data file refers to an affix operation at the desired index position while
maintaining the same data block structure for the whole data file, i.e., inserting a block F corresponds to
shifting all blocks starting with index j + 1 by one slot. An affix operation may affect many rows in the
logical data file matrix F, and a substantial number of computations are required to renumber all the
subsequent blocks as well as re-compute the challenge-response tokens. Therefore, an efficient affix
operation is difficult to support and thus we leave it for our future work.
V. SECURITY ISSUES AND PERFORMANCE ANALYSIS
In this section, we analyse our proposed scheme in terms of security and efficiency. Generally, the
checking scheme is secure if there exists no polynomial-time algorithm that can cheat the verifier with
non-negligible probability; there exists a polynomial-time extractor that can recover the original data files
by carrying out multiple challenges-responses. Our security analysis focuses on the antagonist model
defined in Section II. We also evaluate the efficiency of our scheme via implementation of both file
distribution preparation and verification token pre-computation.
Fig 2. Cloud Security in Various fields
A. Security Strength Against Weak Antagonist
i) Detection Probability against data modification: In our scheme, servers are required to operate on
specified list of tokens. These selected tokens greatly reduce the computational overhead on the server,
while maintaining the detection of the data corruption with high probability. Note that if none of the
specified r rows in the ith verification process are erased or modified, the antagonist avoids the
detection.
ii) Identification Probability for Misbehaving Servers: We have shown that, if the antagonist modifies
the data blocks among any of the data storage servers, our sample checking scheme can successfully
detect the attack with high probability. As long as the data modification is caught, the user will further
determine which server is malfunctioning. This can be achieved by comparing the response values R
with the pre-stored tokens v. The probability for error localization or identifying misbehaving server(s)
can be computed in a similar way. It is the product of the matching probability for sampling check and
the probability of complementary event for the false negative result. Next, we consider the fake denial
probability that R(j)=v(j) when at least one of z blocks are modified. Thus, the identification
probability for misbehaving server(s) is predicted.

B. Security Strength against Strong Antagonist
We analyze the security strength of our schemes against server colluding attack and explain why
blinding the parity blocks can help improve the security strength of our proposed scheme. Redundancy
parity vectors are calculated via multiplying the file matrix F by P, where P is the secret parity generation
matrix we later rely on for storage correctness assurance. If we disperse all the generated vectors directly
after token pre-computation, i.e., without blinding, malicious servers that collaborate can reconstruct the
secret P matrix easily: they can pick blocks from the same rows among the data and parity vectors to
establish a set of m · k linear equations and solve for the m · k entries of the parity generation matrix P.
Once they have the knowledge of P, those malicious servers can consequently modify any part of the data
blocks and calculate the corresponding parity blocks, and vice versa, making their codeword relationship
always consistent. Therefore, our storage correctness challenge scheme would be damaged even if those
modified blocks are covered by the specified rows, the storage correctness check equation would always
hold. To prevent colluding servers from recovering P and making up consistently-related data and parity
blocks, we utilize the technique of adding random perturbations to the encoded file matrix and hence hide
the secret matrix P. We make use of a keyed pseudorandom function f with key k, both of which has been
introduced previously.
C. Performance Evaluation
File Distribution Preparation is implemented for the generation of parity vectors for our scheme.
This experiment is conducted using JAVA on a system with an Intel Core I5 processor, 4096 MB of RAM
and 1TGB Serial ATA drive. Thus the cost decreases when more data vectors are involved. The
performance of our scheme is comparable and even our scheme supports dynamic data operation while is
for static data only. Challenge Token Pre-computation: In our scheme we use fixed number of verification
token t that are determined before file distribution, we can overcome this issue by choosing sufficient large
t in practice.
VI. CONCLUSION
In this paper, we studied the problem of data security in data storage in cloud servers. To guarantee
the correctness of users’ data in cloud data storage, we proposed an effectual and flexible scheme with
explicit dynamic data support, including block revise, erase, and affix. We use erasure correcting code in
the file distribution preparation to provide redundancy parity vectors and guarantee the data dependability.
Our scheme accomplishes the integration of storage correctness insurance and data corruption has been
detected during the storage correctness verification across the distributed servers. Our scheme is highly
efficient and resilient to Byzantine failure, malicious data modification attack, and even server colluding
attacks. We believe that data storage security in Cloud Computing, an area full of challenges and of
dominant significance, is still in its infancy to be identified. We envision several possible directions for
future research on this area. It allows Third Parity Auditor to audit the cloud data storage without
demanding users’ time, probability.
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