DISTRIBUTED SCHEME TO AUTHENTICATE DATA STORAGE SECURITY IN CLOUD COMPUTING

International Journal of Computer Science & Information Technology (IJCSIT) Vol 9, No 6, December 2017
DOI:10.5121/ijcsit.2017.9606 59
DISTRIBUTED SCHEME TO AUTHENTICATE DATA
STORAGE SECURITY IN CLOUD COMPUTING
B. Rakesh1
, K. Lalitha1
, M. Ismail1
and H. Parveen Sultana2
1
Assistant Professor, Department of CSSE, SVEC (Autonomous)
2
Associate Professor, SCOPE, VIT University, Vellore, India
ABSTRACT
Cloud Computing is the revolution in current generation IT enterprise. Cloud computing displaces
database and application software to the large data centres, where the management of services and data
may not be predictable, where as the conventional solutions, for IT services are under proper logical,
physical and personal controls. This aspect attribute, however comprises different security challenges
which have not been well understood. It concentrates on cloud data storage security which has always been
an important aspect of quality of service (QOS). In this paper, we designed and simulated an adaptable and
efficient scheme to guarantee the correctness of user data stored in the cloud and also with some prominent
features. Homomorphic token is used for distributed verification of erasure – coded data. By using this
scheme, we can identify misbehaving servers. In spite of past works, our scheme supports effective and
secure dynamic operations on data blocks such as data insertion, deletion and modification. In contrast to
traditional solutions, where the IT services are under proper physical, logical and personnel controls,
cloud computing moves the application software and databases to the large data centres, where the data
management and services may not be absolutely truthful. This effective security and performance analysis
describes that the proposed scheme is extremely flexible against malicious data modification, convoluted
failures and server clouding attacks.
KEYWORDS
Cloud Computing, Cloud Storage Security, Homomorphic token, EC2, S3
1.INTRODUCTION
The Cloud Computing provides the service over the internet by using the computing resources i.e.
hardware and software. By using software as a service, users can access software and database in
the business model. The management of infrastructure and platform is done by using Cloud
providers. Sometimes, software as a service is referred to as on-demand software. Because of
that Cloud providers charged on pay per use basis. So this causes proponents to get the
computing resources in Outsourcing manner. That’s why the maintenance of hardware and
software is being reduced for users. Introducing of Cloud Computing avoids direct management
of hardware and it causes great convenience to users. Amazon Elastic Compute Cloud (EC2)
and Amazon Simple Storage Service (S3) are the best examples for initiators of Cloud
Computing. In the Cloud Computing the responsibility of local machines is eliminating due to
computing platform shift. Here in the Cloud Computing, the data is integrated so that’s why the
users are showing kindliness towards the cloud service providers.
Providing security to the data is a very important factor which has always been an important
factor of quality of service. Cloud Computing is exhibiting new challenges due to many reasons.
In Cloud Computing the very important thing is that data should be under the control of user. But
by using the traditional cryptographic techniques the users lose the control of data. So traditional

60
Cryptographic primitives cannot be adopted directly. If we use the traditional Cryptographic
primitives directly, we will have conduct the verification for correctness of data stored in the
cloud without having the proper knowledge about the whole data. It is even more challenging
that to provide the continuous assurance of the data safety, if we store different categories of data
stored by different users.
The users are updating the data continuously stored in the cloud. The updation of the data
includes insertion, deletion, modification, appending, reordering etc. During the dynamic data
updation it is very important task that to assure the storage correctness of data. Techniques which
are used can be useful to give assurance to storage correctness without possessing data of user,
where it cannot addresses all the security threat of data storage in cloud, since they all
concentrating on single server situation and a large portion of them don't consider dynamic
operations on data. As a balancing approach, researchers have also proposed protocols which are
distributed for ensuring storage correctness data throughout multiple servers or peers. Again, in
these no distributed schemes is conscious about operations on dynamic data. As a result, their
applicability on cloud data storage can be considerably limited.
In this paper, we present a viable and flexible distributed scheme with dynamic data support
explicitly to verify the user’s data correctness in the cloud. We rely on erasure correcting code in
the file distribution preparation to provide redundancies and guarantee the data dependability.
This development radically diminishes the communication and storage overhead when contrasted
with the conventional techniques of replication-based file distribution. Utilization of
homomorphic token with distributed verification of erasure-coded data, this scheme achieves the
storage correctness insurance as well as data error localization: whenever data corruption has
been identified during the verification of storage correctness, this scheme can localize the data
errors concurrently, i.e., the identification of the misbehaving server(s).
• Compared to many of its predecessors, which only provide binary results about the
storage state across the distributed servers, the challenge-response protocol in our work
further provides the localization of data error.
• Dislike past works for guaranteeing data integrity remotely, on data blocks the new
scheme supports efficient and secure dynamic operations, which includes update, delete
and append.
• The highly efficient security and performance analysis shows that the proposed scheme is
extremely efficient and rigid against Byzantine failure, server colluding attack, , and even
malicious data modification attacks.
1.1 Cloud Data Storage Architecture
The network architecture for cloud data storage is as represented in the figure1. The architecture
is consisting of three different network modules as represented below:
Users: There are different categories of users. The users consisting of both organizations and
individual users. The users are having the data to store in the cloud. The users must have the trust
on the cloud computation.
Cloud Service Provider (CSP) : A cloud service provider has the knowledge and important
resources in building and managing distributed cloud storage servers. A cloud service provider
poses and operates live Cloud Computing systems.
Third Party Auditor (TPA): An optional TPA, who has expertise and capabilities that users may
not have, ills trusted to assess and expose risk of cloud storage services on behalf of the users

61
upon request. In cloud data storage, a user stores his data through a CSP into a set of cloud
servers, which are running in a simultaneous, cooperated and distributed manner. Redundancy of
data redundancy can be engaged with technique of erasure-correcting code to tolerate server crash
or faults as data of the user grows in importance and also size.
After that for the purpose of application, the user interacts with the cloud servers via Cloud
Service Provider (CSP) to retrieve or access his data. In some cases, block level operations has
been performed by user on his data.
Fig 1: Cloud Data Storage Architecture
Most general styles are block insert, delete, update and append are different operations. As
users now not possess their knowledge for data locality, it’s of critical importance to guarantee
users that their data are being properly stored and maintained. That is, users ought to be equipped
with security means that in order that they'll build continuous correctness assurance of their data
stored in cloud even without existence of copy of data locally.
Fig 2: Cloud Server Data Security Architecture
In case if the users don’t necessarily have the time, possibility of resources to check their data,
they can hand over the tasks to an optional trusted Third Party Auditor (TPA) of their relevant
choices. In this model, we presume that the point-to-point communication channels between each
user and the cloud server is authenticated and trustworthy, which can be achieved in practice with
little overhead. Note that we don’t address this type of data privacy issue here, as in Cloud
Computing, data privacy is orthogonal to the problem what we study here

62
2. KEY GENERATION ALGORITHM
2.1. Key generation (µ)
Step 1. Start
2. Obtain parameters (g,h,p).
3. G1 can be generated by g and h.
4. G2 can be generated by prime number p.
5. Set µ=(g,h,ẽ,G1,G2,p,f)
6. f:Zp*{0,1} Zp*Is a one-way hash function
7. User A selects three parameters a1;a2; a3 Zp
*
8. PKA=( ),ska=(a1,a2,a3).
9. Stop.
2.2. Share Key Generation Algorithm
Saharekeygen(SKA,t,m)
Step 1. Start
2. Select m key servers
3. Share secret key SKA to key server ksi
4. SKA,i=(fA,1(i),fA,2(i)) where 1≤i ≤m.
5. Stop.
2.3. Encryption Algorithm
Encryption(PKA,T,m1,m2,…..mk)
Step 1: Start
2. Divide message in to m1,m2,…..mk
3. Calculate cipher text c1,c2,…..ck by using
)
4. Stop.
2.4. Key Recovery Algorithm
Key Recover(SKA,i1 , SKA,i2 , SKA,i3 , SKA,i4…………… , SKA,in )
Step 1. Start
2. User searches for first component a1.
3. if (a1 found)
No need of key recovery.
4. if (a1 not found)
Recovered by using
5. Stop

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2.5. Re Key Generation Algorithm
ReKeyGen(PKA,SKA,ID,PKB)
Step 1. Start
2. Encrypt the data by using SKA.
3. Select a random number e from Zp*.
4.Compute rekey for proxy reencryption by using
5. Stop.
2.6. Proxy Re-Encryption Algorithm
ProxyReEncryption()
Step 1. Start
2. Calculate RK,C’.
3. ReEncrypted symbol can be computed as
4. Stop
3. IMPLEMENTATION
3.1Client Module
In this client module, the server receives query from the client. Based on the query sent by user to
the server it sends the corresponding file to the client. Before doing this process, the client should
authorize. At the server side; it verifies the client name and its password for security process. If it
is verified successfully and received the queries form the client and search those corresponding
files in the database. Finally, it finds that files and sends to the client. If the server finds the
intruder means, it set the alternative Path to those intruders.
3.2. System Module
Representative network architecture for cloud data storage is illustrated in Figure 1. There are 3
network entities which can be identified as below.
3.2.1. User
Users, who have data to be stored in the cloud and rely on the cloud for data computation, consist
of both individual consumers and organizations.
3.2.2. Cloud Service Provider (CSP)
A Cloud Service Provider, who has considerable resources and expertise in managing and
building distributed servers of cloud storage, owns an operative live Computing systems of cloud.

64
3.2.3. Third Party Auditor (TPA)
An optional TPA, who has expertise and capabilities that users may not have, is trusted to assess
and expose risk of cloud storage services on behalf of the users up on request.
3.3. Cloud data storage Module
This is where an user stores his data by using CSP into a group of cloud servers, which are
running concurrently, then the user interacts with the cloud servers via CSP to retrieve or access
his data. In some cases, the user may need to perform block level operations on his own data. So,
users ought to be equipped with security means that in order to build continuous correctness
verification of their data stored in cloud even without existence of copy of data locally. If in case
the use don’t have time and feasibility of resources to monitor their data, they can hand over the
tasks to optional trusted Third Party Auditor(TPA) of their respective choices.
In this model, we presume that the point-to-point communication channels between user and the
cloud server, if user is authenticated and reliable, which can be achieved with little overhead in
practice
3.4. Cloud Authentication Server
The Authentication Server (AS) which functions as any Authentication Server would with a few
more behaviours added to the typical protocol which named as client-authentication protocol.
First, sending information of the client authentication to the masquerading router. The
Authentication Server also functions as authorizing permission control and tickets on the
application network. There is one more optional function that should be supported by the AS is
the updating of client lists, causing a reduction in authentication time or even the removal of the
client as a valid client depending upon the request.
3.5. Unauthorized data modification and corruption module
One of the important issue is to detect any unauthorized modification of data and corruption
effectively, possibly due to server conciliation and/or random Byzantine failures. Also, in the
distributed case when that type of inconsistencies are successfully detected, to find the data error
lies in which server is also of great significance.
3.6. Adversary Module
Threats for Security faced by data storage in cloud can come from two different resources. First, a
CSP can be self-interested, and not trusted and possibly malicious. Its desire is to not only move
data which is not been or is rarely used to a lower tier of storage than accepted for monitoring
reasons, but there is one more chance also attempt to hide a data loss incident which is caused due
to Byzantine failures or management errors and so on.
Second, there may be a chance for existence of adversary who is an economically motivated, it
has capability to compromise a number of cloud data storage servers subsequently in different
time intervals and it is able to modify or delete user’s data for certain period while remaining
undetected by CSPs. Consider two diiferent types of adversary models with different capability
levels.

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3.6.1. Weak Adversary
This adversary is interested in mortifying the data files of user stored on individual servers. If one
server is comprised, this adversary can corrupt the original data files by introducing or modifying
by its own deceptive data to avoid the original data which is retrieved by user.
3.6.2. Strong Adversary
This adversary is the worst case scenario, where we expect that the adversary can compromise all
the other servers which are used for storage so that he/she can intentionally change the data files
as long as they are internally reliable. In fact, this is equivalent to the situation where all the
servers are colluding together to conceal a data loss or corruption occurrence.
4. CONCLUSION AND FUTURE WORK
Here in this paper, we examine data security problem of data storage in cloud, which is essentially
in a distributed storage system. To verify the correctness of users’ data of data storage in cloud,
here we proposed an effective and supple distributed scheme with dynamic data support
explicitly, including block update, append, and delete. Here we depend on erasure-correcting code
for the preparation of distribution of file to provide parity vector of redundancy and guarantees
the dependability of data. By using the homomorphic token of erasure coded data with distributed
verification, our system achieves that integration of storage correctness assurance and localization
of data errors, that is whenever data corruption has been detected during the correctness
verification of data storage in the cloud across the distributed servers. Now, we can almost
guarantee that the concurrent identification of the misbehaving servers. Through this detailed
performance and security analysis, we proved that our scheme is highly resilient and efficient to
malicious data modification attack, Byzantine Failures and server colluding attacks.
We believes that storage security for data in Cloud Computing is an area with full of challenges
and have vital importance, is still in its immaturity now, and there are many research problems are
yet to be identified in this area. We imagine many possible directions for future research in this
area. The most hopeful one we believe that is a model in which public verifiability is compulsory.
Public verifiability, supported in allows the Third Party Auditor (TPA) to audit the data storage in
cloud without demanding users’ time, resources or feasibility. An interesting question here is if
we can build a scheme to achieve both storage correctness assurance and public verifiability of
dynamic data. Besides, with this research on dynamic data storage in cloud, we also plan to
examine the problem of fine-grained error localization of data.
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