Secure Storage Service Using Homomorphic Tokens and Dependable Erasure Coded Data in Cloud Computing

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1 Secure Storage Service Using Homomorphic Tokens and Dependable Erasure Coded Data in Cloud Computing 1 K.SARANYA, 2 I.SIBIYA, 3 M.SASIKALA, 4 D.SUVITHA Assistant Professor, Department of Computer Science and Engineering, Bharathiyar Institute of Engineering for Women, Deviyakurichi. ABSTRACT Cloud storage space enables users to pileup their data and enjoy the on-demand highfeature cloud applications without the trouble of local hardware and software management.though the benefits are clear, such a service is also giving up users' physical control of their data, which certainly causes new security risks toward the correctness of the data in cloud. In order to deal with this new problem and further to accomplish a secure and dependable cloud storage service, we propose in this paper a flexible distributed storage auditing way, utilizing the homomorphic symbol and distributed erasure-coded data. The proposed design allows users to audit the cloud storage with very lightweight communication and computation cost. The auditing result not only secures cloud storage, at the same time accomplishes fast data error localization, the identification of misbehaving server. Considering the cloud data are energetic in nature, the proposed design further supports secure and efficient changing operations on data, including block change, deletion, and add. Analysis shows the proposed layout plan is highly efficient and tough against Confusing failure, evil and cruel data change attack, and even server working (criminally) together attacks.. 1. INTRODUCTION Several trends are opening up the time in history of computing, which is an Internet - based development and use of technology. The ever cheaper and more powerful processors, together with the Software as a Service (SaaS) figuring out changing data centers into pools of calculating service on a huge scale. The increasing network ability and reliable yet flexible network connections make it users to subscribe high quality services from data and software that reside only on remote data centers. On the one hand, although the cloud are much more powerful and reliable than personal calculating devices, broad range of both internal and external force for data still exist. Examples of outages and data loss events of amazing and interesting cloud storage services appear from time to time. On the other hand, since users may not hold a local copy of data, there exist different for CSP to behave dishonestly toward the cloud users regarding the status of their data. For example, to increase the money by reducing cost, it is possible for CSP to throw out rarely accessed data without being detected in an appropriately-timed fashion.csp may even attempt to hide data loss events to maintain reputation. Therefore, data into the cloud is cheaply attractive for the cos t and complex difficulty of long-term large-scale data storage, its missing of offering strong promise of data and availability may interfere with its wide adoption by both business and individual cloud users. In order to accomplish the promises of cloud data honest and availability, the quality of cloud storage service, provide ondemand data correctness checking for cloud users have to be designed. so users no longer have physical possession of data in the cloud prohibits the direct adoption of traditional cryptographic method for the purpose of data protection. Hence, the checking of cloud storage correctness must be conducted without clear knowledge of the whole data files. Meanwhile, cloud storage is not just third party data storage. The datas in the cloud may not only be accessed but also be often updated by the users including add, deletion, change, ap-pending, etc. So, it is also very important to support the combination of this energetic feature into the cloud storage correctness promise, which ISSN: Page 11

2 makes the system design even more challenging. Recently, the importance of making sure of the remote data completeness has been highlighted by the following research works under different system and security models. These ways of doing things, while can be useful to secure the storage correctness without having users local data, are all focusing on single server situation. They may be useful for quality-of-service testing, but does not guarantee the data availability in case of server failures. Although direct applying these ways of doing things to distributed storage (multiple servers) could be easy, the resulted storage checking overhead would be linear to the number of servers. As a completing approach, rese archers havealso proposed distributed rules of conduct for securing storage correctness across multiple servers or peers. However, while providing efficient cross server storage checking and data availability insurance, these layouts are all focusing on static or permanent data. As a result, their abilities of handling energetic data remains unclear, which unavoidably limits their full access incloud storage. In this paper, we propose an effective and flexib le distributed storage checking with clear dynamic data support to secure the correctness and get ability of users' data in the cloud. We depend on erasurecorrecting code in the file distribution preparation to provide unnecessary things and guarantee the data a dependability against Confusing servers, where a storage server may fail in random ways. This construction extremely reduces the communication and storage overhead as compared to the usual copy-based file distribution ways of doing things. By utilizing the homomorphism symbol with distributed checking of erasure-coded data, our big layout accomplishes the storage correctness insurance as well a data error localization: whenever data has been detected during the storage correctness checking, our layout can almost guarantee localization of data errors,, the identification of the misbehaving server(s). Inorder to strike a good balance between error toughness and data patterns, we further explore the math-related property of our symbol computation and erasure-coded data, and demonstrate how to efficiently support energetic operation on data blocks, while maintaining thesame level of storage correctness promise., we also provide theextension of th e proposed main layout to support third-party auditing, where users can safely providechecking tasks to third-party and be worryfree to use the cloud storage services. Our work is among the first few ones in this field to think about distributed data storage security in cloud computing. One can be summarized as the following three aspects: 1) Compared to many of the things that came before it, which only provide binary results about the storage status across the distributed servers, the proposed plan accomplish of storage correctness insurance and data error localizationand the identification of misbehaving server(s). 2) Unlike most prior works for securing remote data, the new plan further supports secure and efficient changing operations on data blocks, including: update, delete, and add. 3) The experiment results will show the proposed plan is highly efficient for long security analysis. ISSN: Page 12

3 II. System Model PROBLEM STATEMENT Three different network things/businesses can b e identified as follows:.user: a thing/business, who has data to be stored in the cloud and depends on the cloud for data storage and computation, can be either business/project or individual customers..cloud Server (CS): a thing, which is managed by Service provider (CSP) to provide data storage service a nd also has significant storage space and computation useful things. Third-Party Person : an optional TPA, who has ability to do things very well and abilities that users may not have, is trusted to evaluate and expose risk of cloud storage services for the users upon request. As users no longer possess their data locally, it is of extr eme importance to make sure of users that their data are being correctly stored and maintained. That is, users should be prepared with security means so that they can make to enforce cloud storage service-level agreement of their stored data even without the exist enceof local copies. In case that users do not necessarily have the time, valuable supplies to watch their data online, they can perform the data auditing tasks to an optional trusted TPA of their choices. However, to securely introduce such a TPA, any possible leakage of user's data toward TPA through the auditing rules of conduct should be prohibited. In our model, we assume that the point-to-point communication channels between each cloud server and the user and reliable, which can be in practice with little overhead. These relation handshakes are left out in the following presentation Adversary Model From user's the way of seeing things, the enemy model has to capture all kinds of threats toward his cloud. Because cloud data do not reside at user's local site but at CSP's address domain, these threats can come from two different sources: internal and external attacks. For internal attacks, a CSP can be self-interested, untrusted, and possibly mal-icious. Not only does it want to move data that has not been or is rarely accessed to a lower level of storage than agreed for moneybased reasons, but it may also attempt to hide a data loss event due to management errors, Confusing failures, and so on. For external attacks, data threatsmay come from outsiders who are beyond the control domain of CSP, for example, the money-based motivated attackers. They may agree some cloud data storage servers in different time periods of time and able to change or delete users' data while remaining undetected by CSP. Therefore, we consider the enemy in our model has the f ollowing abilities, which captures both external and internal threats toward the cloud data.specifically, the e nemy is interested in continuously ruining the user's data files stored on individual servers. Once a server is made up,an enemy can pollute the original data files by changing or introducing its own fake data to prevent the original data from being retrieved by the user. This goes along with the threats from external attacks. In the worst case, the enemy can agree all the storage servers so that he can change the data files as long as they are internally consistent. In fact, this is equal to internal attack case where all servers are assumed working (criminally) together from the early stages of application or service to hide a data loss event. IV. WAY OF WRITING AND PRELIMINARIES The data file to be stored. We assume that F can be repre sented as a connected combination of m equal-sized data vectors, each consisting of l blocks A--The breaking up and moving away matrix used for Reed-Solomon coding. G--The (translated /put into secret code) file matrix, which includes a set of n ¼ m þ k vectors, each consisting of l blocks. Over-- a version number bound with the index for individual ISSN: Page 13

4 blocks, which records the times the block has been changed. At first we assume ver is 0 for all data blocks. sverij--the seed for PRF, which depends on the file name, blockindex i, the server position j as well as the optional block version number ver. III. ENSURING CLOUD DATA STORAGE In cloud data storage system, users store their data in the cloud and no longer possess thedata locally. So, the corr ectness and availability of the data files being stored on t hedistributed cloud servers must be guaranteed. One of t he key issues is to effectively detectany unauthorized dat a change and misbehaving, possibly due to server and /or random Confusing failures Besides, in the distributed case when such inconsistencies are successfully detected, to find which server the data error lies in is als oof great importance,since it can always be the first step to fast recover the storage errors and/or identifying possible threats of external attacks. To deal with these problems, our main plan for making sure of cloud data storage is presented in this section. The first part of the section is is dedicated to a review of basic tools from coding explanation that is needed in our big plan for file distribution across cloud servers. Then, the homomorphi c symbol is introduced. The symbolic computation function we are thinking about belongs to a family of universal hash function, chosen to preserve the homomorphic properties, which can be perfectly combined with the checking of erasure-coded data, it is shown how to get a challenge-response rules of conduct for the storage correctness as well as identifying misbehaving servers. The procedure forfile retrieval and error recover y based on erasure-correcting code is also organized and listed. Finally, we describe how to extend our plan to third party auditing with only small change of the main design. FILE RETRIEVAL AND ERROR RECOVERY Since our layout of file matrix is well-thoughtout, the user can reconstruct the original file by downloading the data vectors from the first m servers, assuming that they return the c orrect response values. Notice that our checking big plan is basedon random spot-checking, so the storage correctness promise is a probabilistic one. However,by choosing system limits appropriately and conducting enough times of checking, we can guarantee the successful file retrieval with high probability. On the otherhand, whenever the data theft is detected, the comparison of precomputed symbols and received response values can guarantee the identification of misbehaving server(s), which will be discussed shortly. Therefore,the user can always ask servers to send back blocks of the r rows specified in the challenge and to correct blocks by erasure correction, shown in Set of computer instructions, as long as the number of identified misbehaving servers is less. The newly recovered blocks can then be redistributed to the misbehaving servers to maintain the correctness of storage. Algorithm Error Recovery. procedure Assume the block things have been detected among the specified r rows; Assume s _ k servers have been identified misbehaving Download r rows of blocks from servers; Treat s servers as erasures and recover the block s. Resend the recovered blocks to corresponding s ervers. end procedure Toward Third Party Auditing The user does not have the time, to perform the storage correctness checking,the can optionally transfer power to this job to an independent thirdparty person who carefully checks business records, making the cloud storage publicly. However,as pointed out by the recent work to securely i ISSN: Page 14

5 ntroduce an effective TPA, the auditing process should bring in no new weaknesses toward user data privacy. Namely, TPA should not learn user's data content through thedata auditing. Now we show that with onl ysmall change, our rules of conduct can support privacypreserving third party auditing. parities. This part of operation has to be carried out by the user, since only he knows the secret matrix P. Besides, to make sure of the changes of data blocks correctly reflected in the cloud address domain, the user also needs to change the similar The new design is based on the observation of linear pro perty of the equality vector very extreme process. Recall that the reason of veryextreme process is for protection of the secret matrix P against cloud servers. However, this can be accomplished either by hide the equality vector or hiding the data vector So, if we blind data vector before file distrib ution, then the storage checking job can be successfully transferred power to third party auditing in a privacy-preserving manner PROVIDING DATA OPERATION SUPPORT So far, we assumed that F represents static data. This m odel may fit someapplication pictures, such as libraries a nd scientific datas ets. However, in cloud data storage, there are many possible situations where data stored in the cloud is changing, like electronic documents, photos, or log files, etc. Therefore, it is extremely important to think about the changing case, where a user may wish to perform different block-level operations of update, delete, and add to change the data file while maintaining the storage correctness promise. Since data do not reside at users' local site but at Service provider's address domain, supporting changing data operation can be quite challenging. On the one hand, CSP needs to process the data patterns request without knowing the secret keying material.on the other hand, users need to make sure that all the changing data operation request has been faithfully processed by CSP. To deal with this problem, we briefly explain our approach way(s) of doing things here and provide the details later. For any data changing operation, the user must first create the similar resulted file blocks and storage checking symbols to change something take care of someone the changes on data blocks. V. CONCLUSION In this paper, we investigate the problem of data security in clouddata storage, which is almost a distributed storage system. To accomplish the promises of cloud data completeness and availability, enforce the quality of dependable cloud storage service for users, we propose an effective and flexible distributed plan with changing data support, including block update, delete, and add. We depend on erasure-correcting code in the file distribution preparation to provide unnec essary thing equality vectors and guarantee the data dependabili ty. By utilizing the homomorphic symbol with distributed checking of erasure coded data, our plan to accomplish the combination of storage correctness insurance and data error localizati on, whenever data has been detected during the storage correctness checking across the distributed servers, we can almost guarantee the identification of the misbehaving server(s). Considering the time, computation useful valuable supplies, and even the related online heavy load of users, we also provide the extension of the proposed main ISSN: Page 15

6 plan to supportthird-party auditing, where users can safely transfer power to checking tasks to third party people and be worry-free to use the cloud storage services. Through described results, we show that our plan is highly efficient and tough to Confusing failure, evil and cruel data change attack, and even server working (criminally) together attacks. Int l Conf. Security and Privacy in Comm. Netowrks (SecureComm 08), pp. 1-10, [15] Q. Wang, C. Wang, J. Li, K. Ren, and W. Lou, Enabling Public Verifiability and Data Dynamics for Storage Security in Cloud Computing, Proc. 14th European Conf. Research in Computer Security (ESORICS 09), pp , REFERENCES [1] C. Wang, Q. Wang, K. Ren, and W. Lou, Ensuring Data Storage Security in Cloud Computing, Proc. 17th Int l Workshop Quality of Service (IWQoS 09), pp. 1-9, July [2] Amazon.com, Amazon Web Services (AWS), amazon.com, [3] Sun Microsystems, Inc., Building Customer Trust in Cloud Computing with Transparent Security, offers/details/sun_transparency.xml, Nov [4] K. Ren, C. Wang, and Q. Wang, Security Challenges for the Public Cloud, IEEE Internet Computing, vol. 16, no. 1, pp , [5] M. Arrington, Gmail Disaster: Reports of Mass Deletions, Dec [6] J. Kincaid, MediaMax/TheLinkup Closes Its Doors, July [7] Amazon.com, Amazon S3 Availability Event: July 20, 2008, July [8] S. Wilson, Appengine Outage, /appengine_outage.php, June [9] B. Krebs, Payment Processor Breach May Be Largest Ever, payment_processor_breach_may_b.html, Jan [10] A. Juels and B.S. Kaliski Jr., PORs: Proofs of Retrievability for Large Files, Proc. 14th ACM Conf. Computer and Comm. Security(CCS 07), pp , Oct [11] G. Ateniese, R. Burns, R. Curtmola, J. Herring, L. Kissner, Z.Peterson, and D. Song, Provable Data Possession at Untrusted Stores, Proc. 14th ACM Conf. Computer and Comm. Security (CCS 07), pp , Oct [12] M.A. Shah, M. Baker, J.C. Mogul, and R. Swaminathan, Auditing to Keep Online Storage Services Honest, Proc. 11th USENIX Workshop Hot Topics in Operating Systems (HotOS 07), pp. 1-6, [13] M.A. Shah, R. Swaminathan, and M. Baker, Privacy- Preserving Audit and Extraction of Digital Contents, Cryptology eprint Archive, Report 2008/186, [14] G. Ateniese, R.D. Pietro, L.V. Mancini, and G. Tsudik, Scalable and Efficient Provable Data Possession, Proc. Fourth ISSN: Page 16

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