A survey on data integrity issues and solutions dynamic data storage on cloud servers ijaerdv05i0386

Scientific Journal of Impact Factor (SJIF): 5.71

e-ISSN (O): 2348-4470 p-ISSN (P): 2348-6406

International Journal of Advance Engineering and Research Development Volume 5, Issue 03, March -2018

A SURVEY ON DATA INTEGRITY ISSUES AND SOLUTIONS: DYNAMIC DATA STORAGE ON CLOUD SERVERS Mrs. G.Kiruthiga1, Dr. Mary Vennila.S2 1

Research Scholar, PG and Researce department of Computer Science, Presidency College, Chennai.

2 PG & Research Department of Computer Science, Presidency College

Abstract- Cloud storage has become essential for storing large volume of data and to meet the need to access data from any location. In recent years, vulnerabilities in data storage in the Cloud have come to light. Due to virtualization storage concept of the Cloud, these vulnerabilities arise and hence lead to Cloud storage issues. – Data integrity tries to give reassurance that data is uncorrupted – Data integrity involves the management of consistency, accuracy and trust worthiness of data in their entire life cycle period – This survey paper discusses these issues and analyzes solutions that address these issues.

Keywords - Cloud data storage; data integrity; security issues; static data; dynamic data.

I. INTRODUCTION Cloud computing is a distinct business model, wherein a client corporation can avail the services provided by Cloud Service Provider (CSP) which broadly includes data storage and computational capabilities. The client would pay the CSP based on resources actually used by them. Due to the large volume of data handled by CSPs and keeping in view the varied originating points of such data, it is imperative for the CSPs to ensure data integrity and release resources as a useful utility in a dynamic environment. Small corporations/organizations may find it expensive to build the infrastructure required for storing their expanding data volumes and hence resort to services offered by CSPs. Cloud storage and computations help to reduce the costs associated with storage and maintenance of data. There also arises the need to improve the cloud storage business standards. The client companies have to negotiate with different terms and conditions of the CSPs and always face a marginal risk of their data being lost from the cloud data centre.

As data generation grows exponentially and outpaces data storage capabilities at their facilities, the smaller companies find it expensive to regularly replace or upgrade their hardware on every occasion when the storage capacity becomes full and retaining their full data bank may prove to be a daunting undertaking. Storage and computing outsourcing to CSPs allows such companies to lower the cost/expenses associated with storage and also efficiently manage their employees. This option also additionally assures them of a reliable storage mechanism for their critical records, thus eliminating the hazard of losing records due to hardware failures. But storage of user records in the cloud, inspite of its blessings, has many potential safety issues which need to be extensively investigated for making a dependable strategy related to eliminating data loss and simultaneously reducing storage costs. Common risks include information/data integrity at cloud storage and their authentication (uncorrupted data).

II. DATA INTEGRITY Data integrity refers to the process of making a database remain an accurate mirrored image of the universe of discourse it is modeling or representing. At times, there could be only narrow communication between the facts saved in the data base and the actual real world applications. Data integrity focused in terms of data protection is the assurance that data can most prominently accessed or is available for modifications for the legal computational requirements and for verifying records.

Data integrity is essentially one of the few and most important challenges in cloud storage and computing.

Data integrity must ensure to provide reliable data with high quality, accuracy and unmodified in any way. After transacting with the cloud data centre, the client depends on the cloud data storage centre to provide back reliable data to them and trust that their data and algorithms are protected by CSP with the highest security mechanism. But this hope may fail at times, leading to clients’ data being lost or modified. At times, the CSPs may adopt dishonest business practices and may discard statistical data that is no longer or rarely accessed by clients, in order to save the garage space or they may hold fewer replicas than promised.

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International Journal of Advance Engineering and Research Development (IJAERD) Volume 5, Issue 03, March-2018, e-ISSN: 2348 - 4470, print-ISSN: 2348-6406

Data integrity Data privacy Data security issues

Cloud Storage

Data availability Data confidentiality

Figure 1. Organization of data security in cloud computing Furthermore, the CSPs may cover up data loss and still maintain a false claim that data are nevertheless effectively saved within the Cloud. Therefore, one of the biggest concerns with cloud data storage is to perform data integrity verification at untrustworthy servers. In their efforts towards solving the problem of data integrity checking, many researchers have proposed distinct systems and security measures.

III. DATA INTEGRITY ISSUES When transmitting data to/from the cloud data centre, the cloud data may suffer some harm. Eventhough the data and the computation are outsourced to a remote server, the data integrith ought to be maintained and reviewed continuously, to check that the data and the computation are intact with each other. The integrity approach maintains a record to be saved from any unauthorized changes. Any modification to the data has to be detected.

Figure 2. Issues around the data integrity The clients should expect computation integrity execution and the computation executions must be saved from malware, insider corruption or any malicious use that would change the program execution and render a wrong or faulty outcome. Any deviations from the regular, structured computation should be detected. Integrity have to be checked at both the data level and the computational degree level. Data integrity verification ought to help in identifying and restoring misplaced data or notifying the client if there had been any data manipulation.

The following indicates the ways in which data integrity could be violated: 3.1. Data Loss or Manipulation The clients/user companies have a large variety of data files that they store on the Cloud platform. The CSPs offer cloud storage as well as computing capabilities through Software as a Service (Saas) model, wherein, the client companies need not invest huge sums to purchase and install expensive operational softwares; their data as well as all computational records as well are stored on the Cloud. The user clients can access such files online anytime, 24x7x365 days, as and when they require.

Such data of the clients need to be protected and preserved without distorting their accuracy.

Due to their very nature, the Cloud could prove to be unsecure, as some data could be tampered with, could be misplaced or lost or replaced with a wrong set of data and the data of the clients could fall in the hands of unauthorized users. Intentional, @IJAERD-2018, All rights Reserved

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International Journal of Advance Engineering and Research Development (IJAERD) Volume 5, Issue 03, March-2018, e-ISSN: 2348 - 4470, print-ISSN: 2348-6406 deliberate damage to the data also cannot be ruled out. Due to operational deficiencies at the end of the CSPs, the data could be wrongly labeled or incorrect backups could be created/stored, leading to a wrong set of data being pulled out when the clients need them. The vulnerabilities at CSPs could invite hackers/attackers to play their malware/hacking softwares to destroy or destabilize the data stored on the Cloud servers.

3.2 Untrusted Remote Server Performing Computation on Behalf of User: CSPs provide not only a storage platform but also intensive computational capabilities. Complex programs or ERPs or similar software tasks that require complex mini super-computer like capabilities can be accessed through the Cloud. But, the CSPs are not under the security firewalls of the client companies and they have their own security protocols, which may or may not be adequate to safeguard the interests of the clients. So, the clients would not be assured of the computational integrity of their operations. Sometimes, deviations in computations on the Cloud may go undetected as well due to security loopholes or deliberate human actions at the end of the CSPs. Further, since the data storage resources at the disposal of the CSPs have monetary value, the provider may intentionally allot a portion of the resource allocated to a particular client to another client either temporarily or on a long term basis, leading again to security breaches. The CSPs’ underlying systems and procedures and protocols always pose a data integrity and security risk, which cannot be overlooked.

IV. DATA INTEGRITY CHALLENGES This phase will try to recognition on few mechanism. Also, provide survey at the unique strategies of data integrity and there terms. The simple schemes for data integrity in cloud are Provable statistics ownership (PDP) and Proof of Retrievability (PoR). The subsequent section describes the privacy techniques for data integrity.

This section deals with a few techniques and tools that are available and surveyed as well as effective in the market to deal with the challenges posed by attackers on the Cloud platform. These techniques offer to assure data integrity. Two such techniques are Provable Data Possession (PDP) and Proof of Retrievability (PoR). The subsequent section describes these techniques for safeguarding data integrity.

4.1. Provable Data Possession (PDP) Provable data possession (PDP) is a method for implicit data integrity over remote data centres. In PDP, the client user who has stored data at an untrue server can confirm that the server possesses the authentic data without retrieving it. Ateniese et al. are the first to keep in mind public audit ability of their described “provable data possession” model for guaranteeing possession of documents on data storages.

Figure.2 Protocol for provable data possession In paper [5] under Reference, the authors propose Message Authentication Code [MAC] based total PDP to ensure data integrity of files F stored on cloud storage in a quite simple manner .The data owner computes a MAC of the whole document with a fixed number of secret keys and stores them domestically before outsourcing it to a CSP. The client customer keeps simplest the computed MAC on his neighborhood storage, sends the report to the CSP. Every time a verifier @IJAERD-2018, All rights Reserved

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International Journal of Advance Engineering and Research Development (IJAERD) Volume 5, Issue 03, March-2018, e-ISSN: 2348 - 4470, print-ISSN: 2348-6406 needs to check the data integrity of document, the client sends a request to get the file from CSP, reveals a secured key to the cloud server and asks to restructure the MAC of the whole report, and compares the re-structured MAC with the existing one.

This PDP includes the subsequent wide variety of strategies to perform integrity check at the data.      

Naïve Method Original Provable Data Possession Proof of Retrievability (PoR) Scalable PDP Dynamic PDP High Availability and Integrity Layer for Cloud Storage (HAIL)

This method is the simplest and most easily adaptable technique for static data. It is possible to test data integrity inside the distributed storage and also the redundancy. Here proof is generated that is unbiased of the data size and it is compact in length. HAIL makes use of the pseudo-random feature, message, authentication codes (MACs) and is effective for testing data integrity.

4.2. Proof of Retrievability (PoR) : Generally, in cloud computing, the application software and the connected databases are moved to a centralized data storage centre; in such a scenario, the management of data and the CSP’s services may not be trustworthy. Under this PoR data integrity assurance mechanism, the cloud storage server and cloud audit server work in tandem. The cloud audit server used by the client, pre-processes the data, before uploading to the main cloud server. PoR outsources the task of tag creation (which consumes significant resources) to the audit server and the client users are spared this task.

V. COMPARATIVE STUDY A comparative study of the various methodologies surveyed and reviewed so far, is provided below: Techniques Verifiable Homomorphic Technique

Methodologies used to verify Homomorphism is a map. f:XY between two groups such that f(group1 ⊕ group2)=f(group1) ⊗ f(group2) for all group1, group2 ∈ X, where ⊕ denotes the operation in X and⊗ denotes the operation in Y.

CPDP Model

KeyGen(1): takes a secured parameter input, returns a secret key sk or a public-secret key aspair (pk, sk);

Provable Data Possession

Key Generation Algorithm

PDP Scheme based on MAC

Message Authentication Code

Scalable PDP

Cryptographic Hash function & symmetric key encryption

Dynamic PDP Proof of Retrievability

Rank-based authenticated skip list.

Encryption

POR based on keyed hash function hk

Key Hash Function

High Availability Integrity Layer (HAIL)

MAC, Pseudorandom function, Hash Function

Cloud data storage is basically a distributed data storage concept. The above study has analyzed the data security challenges encountered in such a system. The survey has studied the techniques that could be adopted to ensure that client users could store and use their data as it should be, without any violation of data integrity, keeping data intact at all times within the cloud. To provide redundancy, erasure correcting code could be used inside file distribution preparation. Cloud is nothing but a third party data warehouse and hence providing operational support for dynamic operations is a must and above mentioned schemes tend to maintain data storage correctness assurance even if users modify, delete or append their data files in the Cloud. @IJAERD-2018, All rights Reserved 1128

International Journal of Advance Engineering and Research Development (IJAERD) Volume 5, Issue 03, March-2018, e-ISSN: 2348 - 4470, print-ISSN: 2348-6406 VI. CONCLUSION Cloud is chiefly used for storage of huge volumes of data and for handling complex computations. The major challenge faced by client companies is that of data integrity, as the client loses control over their data, once uploaded onto the servers of a CSP. Many techniques have been developed and are available to enable clients to measure and review their control over their data. So that any unauthorized access by the CSP’s server or other attackers to modify/update/delete data could be tracked and controlled.

Retaining integrity of data on the Cloud is a major cyber security issue and considering the importance of data integrity, this paper has analyzed different, existing data integrity techniques, along with their advantages and disadvantages (merits & demerits). One would be able to easily conclude that there is a need to design and implement an efficient, dynamic and effective data integrity technique while utilizing the Cloud services.

REFERENCES [1] Secuirty Storage in the Cloud Computing: A RSA-based Assumption Data Integrity Check without Original Data, “2010 International Coriference on Educational and Information Technology (ICEIT 2010)”, Zhang lianhong, Chen Hua. [2] J. Ruiter and M. Warnier, Privacy regulation for cloud computing, compliance and implementation in theory and practice, article.

[3] G. Ateniese, R. D. Pietro, L. V. Mancini, and G. Tsudik, “Scalable and Efficient Provable Data Possession,” in Proceedings of SecureComm ’2008.

[4] S. Subashini and V. Kavitha, “A survey on security issues in service delivery models of cloud computing,” Journal of Network and Computer Applications, vol. 34, no. 1, pp. 1 – 11, 2011.

[5] R Sravankumar and Saxena,”Data integrity proofs in cloud storage”, Third International Conference on Communication Systems and Networks (COMSNETS), Bangalore, India 2011.

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