Techniques for Efficiently Ensuring Data Storage Security in Cloud Computing Vasu Raju † , Raj Kumar †† , and Anand Raj ††† vasuraju_1255@yahoo.com naaniraj@gmail.com , anandsofttech@gmail.com † M.Tech (C.S.E), S.r.Engg College, J.N.T.U. University, Warangal, A.p, 506002 India †† Associative Professor in CSE Dept, S.r.Engg College, University, Warangal, A.p, 506002 India ††† Sr.Assistant Professor in CSE Dept, S.r.Engg College, University, Warangal, A.p, 506002 India Summary The Cloud Computing is the next generation architecture of IT Enterprise. It moves the application software and databases to the large data centers, where the management of the data and services may not be fully trustworthy. Here, focus is on cloud data storage security, an important aspect of quality of service. To ensure the correctness of users’ data in the cloud, we propose an effective and flexible distributed scheme with two salient features. By utilizing the homomorphic token with distributed verification of erasure-coded data, the scheme achieves the integration of storage correctness and data error localization. The new scheme further supports secure and efficient dynamic operations on data blocks, including: data update, delete and append. Extensive security and performance analysis shows that the proposed scheme is efficient and resilient against Byzantine failure, malicious data modification attack, and server colluding attacks. Key words: Homomorphic token, Dynamic operations, Byzantine failures, CSP. 1. Introduction Cloud Computing is an Internet-based development. Users can now subscribe high quality services from data and software that reside solely on remote data centers. The pioneer of Cloud Computing Vendors are Amazon Simple Storage Service (S3) and Amazon Elastic Compute Cloud (EC2) [1]. From the perspective of data security, Cloud Computing inevitably poses new challenging security threats for number of reasons. Firstly, cryptographic primitives for the purpose of data security protection can not be directly adopted due to the users’ loss control of data under Cloud Computing. Therefore, verification of correct data storage in the cloud must be conducted without explicit knowledge of the whole data. Considering various kinds of data for each user stored in the cloud and the demand of long term continuous assurance of their data safety, the problem of verifying correctness of data storage in the cloud becomes even more challenging. Secondly, the data stored in the cloud may be frequently updated by the users, including deletion, modification, appending, reordering, etc. This dynamic feature also makes traditional integrity insurance techniques futile and entails new solutions. Last but not the least, the deployment of Cloud Computing is powered by data centers running in a simultaneous, cooperated and distributed manner. Individual user’s data is redundantly stored in multiple physical locations to further reduce the data integrity threats. Therefore, distributed protocols for storage correctness assurance will be of most importance in achieving a robust and secure cloud data storage system in the real world. Recently, the importance of ensuring the remote data integrity has been highlighted by the following research works [2]–[6]. These techniques, while can be useful to ensure the storage correctness without having users possessing data, can not 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. Our contribution can be summarized as the following three aspects: (i) Compared to many of its predecessors, the proposed work provides the localization of data error. (ii) For ensuring remote data integrity, new scheme supports secure, efficient dynamic operations on data blocks. (iii) Extensive security and performance analysis shows that the proposed scheme is highly efficient and resilient against Byzantine failure, malicious data modification attack, and even server colluding attacks. 2. Data Storage Security 2.1. Notations Of Consideration • F – The data file to be stored. We assume that F can be denoted as a matrix of m equal-sized data vectors, each consisting of l blocks. All Data blocks are represented as elements in Galois Field GF for p = 8 or 16. • A – The matrix used for Reed-Solomon coding. Vasu Raju et al, Int. J. Comp. Tech. Appl., Vol 2 (5), 1717-1721 IJCTA | SEPT-OCT 2011 Available online@www.ijcta.com 1717 ISSN:2229-6093