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1 ISSN Vol.03,Issue.11 June-2014, Pages: Secure Cloud: Storage with Privacy Preserving Public Auditing and Data Dynamics Dept of CSE, Malabar College of Engineering and Technology, Kerala, India, Abstract: Cloud storage enables users to remotely store their data and enjoy the on-demand high quality cloud applications without the burden of local hardware and software management. Cloud computing is the delivery of computing as a service rather than a product, whereby shared resources, software, and information are provided to computers and other devices as a utility over a network. Cloud computing entrusts services with a user's data, software and computation on a published application programming interface over a network where data owners can remotely store their data in the cloud to enjoy ondemand high-quality applications and services from a shared pool of configurable computing resources. While data outsourcing relieves the owners of the burden of local data storage and maintenance, it also eliminates their physical control of storage dependability and security. So correctness of data and security is a prime concern. Security in cloud is achieved by signing the data block before sending to the cloud. Signing is performed using sha-1 algorithm which is more secure compared to other algorithms. A third party auditor on behalf of the cloud user will verify the integrity of the data stored in the cloud. Keywords: Cloud Computing, Data Dynamics, Data Integrity, Error Localization, Third Party Auditor. I. INTRODUCTION Cloud computing is a computing style where massively scalable and elastic IT related capabilities are provided as a service to external customers using internet technologies. It is a network based computing that takes place over internet. It is basically a step on from utility computing. It can be considered as a group or collection of integrated and networked hardware, software and internal infrastructure (platform). Cloud Computing has been envisioned as the next generation architecture of IT enterprise, due to its long list of unprecedented advantages in the IT history: on-demand selfservice, ubiquitous network access, location independent resource pooling, rapid resource elasticity, usage-based pricing and transference of risk. As a disruptive technology with profound implications, Cloud Computing is transforming the very nature of how businesses use information technology. One fundamental aspect of this paradigm shifting is that data is being centralized or outsourced into the Cloud. From users perspective, including both individuals and IT enterprises, storing data remotely into the cloud in a flexible on-demand manner brings appealing benefits: relief of the burden for storage management, universal data access with independent geographical locations, and avoidance of capital expenditure on hardware, software, and personnel maintenances, etc [1],[2]. Several trends are opening up the era of cloud computing, which is an Internet-based development and use of computer technology. The ever cheaper and more powerful processors, together with the Software as a Service (SaaS) computing architecture [4], are transforming data centres into pools of computing service on a huge scale. The increasing network bandwidth and reliable yet flexible network connections make it even possible that users can now subscribe high quality services from data and software that reside solely on remote data centres. With cloud storage data storage capacity can be hired to others. Cloud storage enables users to remotely store their data and enjoy the on-demand high quality cloud applications without the burden of local hardware and software management. Such a service is also relinquishing users physical possession of their outsourced data, which inevitably poses new security risks toward the correctness of the data in cloud. Moving data into the cloud offers great convenience to users since they don t have to care about the complexities of direct hardware management. The pioneer of cloud computing vendors, Amazon Simple Storage Service (S3), and Amazon Elastic Compute Cloud (EC2) [3] are both well-known examples. Cloud computing is a network of computers used for remote storage and maintenance. A number of characteristics define cloud applications and services and infrastructure. While Cloud Computing makes these advantages, it also brings new and challenging security threats towards users outsourced data. Since cloud service providers (CSP) are separate administrative entities, data outsourcing is actually relinquishing user s ultimate control over the fate of their data [3] [4]. As a result, the correctness of the data in the cloud is being put at risk due to the following reasons. First of all, although the infrastructures under the cloud are much more powerful and reliable than personal computing devices, they are still facing the broad range of both internal and external threats for data integrity [10]-[12]. Examples of outages and security breaches of 2014 SEMAR GROUPS TECHNICAL SOCIETY. All rights reserved.

2 cloud services appear from time to time. Secondly, for the benefits of their own, there exist various motivations for cloud service providers to behave unfaithfully towards the cloud users regarding the status of their outsourced data. Examples include cloud service providers, for monetary reasons, reclaiming storage by discarding data that has not been or is rarely accessed, or even hiding data loss incidents so as to maintain a reputation In short, although outsourcing data into the cloud is economically attractive for the cost and complexity of long-term large-scale data storage, it does not offer any guarantee on data integrity and availability. This problem, if not properly addressed, may delay the successful deployment of the cloud architecture. As users no longer physically possess the storage of their data, traditional cryptographic primitives for the purpose of data security protection cannot be directly adopted. Thus, how to efficiently verify the correctness of outsourced cloud data without the local copy of data files becomes a big challenge for data storage security in Cloud Computing. Note that simply downloading the data for its integrity verification is not a practical solution due to the expensiveness in input output cost and transmitting the file across the network. Besides, it is often insufficient to detect the data corruption when accessing the data, as it might be too late for recover the data loss or damage. Considering the large size of the outsourced data and the user s constrained resource capability, the ability to audit the correctness of the data in a cloud environment can be formidable and expensive for the cloud users Therefore, to fully ensure the data security and save the cloud users computation resources, it is of critical importance to enable public auditability for cloud data storage so that the users may resort to a third party auditor (TPA), who has expertise and capabilities that the users do not, to audit the outsourced data when needed. Based on the audit result, TPA could release an audit report, which would not only help users to evaluate the risk of their subscribed cloud data services, but also be beneficial for the cloud service provider to improve their cloud based service platform. In a word, enabling public risk auditing protocols will play an important role for cloud economy to become fully established; where users will need ways to assess risk and gain trust in Cloud. Recently, the notion of public auditability has been proposed in the context of ensuring remotely stored data integrity under different systems and security models. Public auditability allows an external party, in addition to the user himself, to verify the correctness of remotely stored data. However, most of these schemes do not support the privacy protection of users data against external auditors, i.e., they may potentially reveal user data information to the auditors [9]-[12]. This severe drawback greatly affects the security of these protocols in Cloud Computing. From the perspective of protecting data privacy, the users, who own the data and rely on TPA just for the storage security of their data, do not want this auditing process introducing new vulnerabilities of unauthorized information leakage towards their data security. Exploiting data encryption before outsourcing is one way to mitigate this privacy concern, but it is only complementary to the privacy-preserving public auditing scheme to be proposed in this paper. Without a properly designed auditing protocol, encryption itself cannot prevent data from flowing away towards external parties during the auditing process. Thus, it does not completely solve the problem of protecting data privacy but just reduces it to the one of managing the encryption keys. Unauthorized data leakage still remains a problem due to the potential exposure of encryption keys. Therefore, how to enable a privacy-preserving third-party auditing protocol, independent to data encryption, is the problem to be solved. My work is to support privacypreserving public auditing in Cloud Computing, with a focus on data storage. Besides, with the prevalence of Cloud Computing, a foreseeable increase of auditing tasks from different users may be delegated to TPA. As the individual auditing of these growing tasks can be tedious and cumbersome, a natural demand is then how to enable TPA to efficiently perform the multiple auditing tasks in a batch manner, i.e., simultaneously. To address these problems, my work utilizes the technique of public key based homomorphic authenticator which enables TPA to perform the auditing without demanding the local copy of data and thus drastically reduces the communication and computation overhead as compared to the straightforward data auditing approaches. The aggregation and algebraic properties of the authenticator further benefit design for the batch auditing. Meanwhile, cloud storage is not just a third party data warehouse. The data stored in the cloud may not only be accessed but also be frequently updated by the users [14], [15], [16], include insertion, deletion, modification, appending, etc. Thus, it is also imperative to support the integration of this dynamic feature into the cloud storage correctness assurance, which makes the system design even more challenging Specifically, My work can be summarized as the following three aspects: 1. Motivate the public auditing system of data storage security in Cloud Computing and provide a privacypreserving auditing protocol, i.e., my scheme supports an external auditor to audit user s outsourced data in the cloud without learning knowledge on the data content. 2. Support scalable and efficient public auditing in the Cloud Computing. In particular, our scheme achieves batch auditing where multiple delegated auditing tasks from different users can be performed simultaneously by the TPA. 3. Unlike most prior works for ensuring remote data integrity, the scheme further supports secure and efficient dynamic operations on data blocks, including: update, delete, and append. II. PROBLEM STATEMENT A. System Model Three different network entities in cloud network can be identified as follows (see fig 1): User: an entity, who has data to be stored in the cloud and relies on the cloud for data storage and

3 Secure Cloud: Storage with Privacy Preserving Public Auditing and Data Dynamics computation, can be either enterprise or individual customers. Cloud Server (CS): an entity, which is managed by cloud service provider (CSP) to provide data storage service and has significant storage space and computation resources (we will not differentiate CS and CSP hereafter). Third-Party Auditor: 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 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. Data redundancy can be employed with a technique of erasure correcting code to further tolerate faults or server crash as user s data grow in size and importance. Thereafter, for application purposes, the user interacts with the cloud servers via CSP 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 we are considering are block update, delete, insert, and append. As users no longer possess their data locally, it is of critical importance to ensure users that their data are being correctly stored and maintained. That is, users should be equipped with security means so that they can make continuous correctness assurance (to enforce cloud storage service-level agreement) of their stored data even without the existence of local copies. In case those users do not necessarily have the time, feasibility or resources to monitor their data online, they can delegate the data auditing tasks to an optional trusted TPA of their respective choices. However, to securely introduce such a TPA, any possible leakage of user s outsourced data toward TPA through the auditing protocol should be prohibited. Data Integrity is very important among the other cloud storage issues. Because data integrity ensured that data is of high quality, correct, consistent and accessible. After moving the data to the cloud, owner hopes that their data and applications are in secured manner. But that hope may fail some times (i.e.) the owner s data may be altered or deleted. In that scenario, it is important to verify if one s data has been tampered with or deleted. To validate data, often a user must download the data. If the outsourced data is very large files or entire file systems, such downloading to determine data integrity may become prohibitive in terms of increased cost of bandwidth and time, especially if frequent data checks are necessary. My project propose a method that, owner need not download the data or files to check the integrity and it provides the proofs that data is stored at a remote storage in the cloud is not modified by anyone and there by integrity of the data is assured. Data integrity threats toward users data can come from both internal and external attacks at CS. These may include: software bugs, hardware failures, bugs in the network path, economically motivated hackers, malicious or accidental management errors, etc. Besides, CS can be self-interested. For their own benefits, such as to maintain reputation, CS might even decide to hide these data corruption incidents to users. Using third-party auditing service provides a cost-effective method for users to gain trust in cloud. We assume the TPA, who is in the business of auditing, is reliable and independent. However, it may harm the user if the TPA could learn the outsourced data after the audit. B. Adversary Model Adversary model has the following capabilities, which captures both external and internal threats toward the cloud data integrity. Specifically, the adversary is interested in continuously corrupting the user s data files stored on individual servers. Once a server is comprised, an adversary 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. This corresponds to the threats from external attacks. In the worst case scenario, the adversary 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 internal attack case where all servers are assumed colluding together from the early stages of application or service deployment to hide a data loss or corruption incident Fig 1: Cloud storage service architecture. C. Design Goals To ensure the security and dependability for cloud data storage under the aforementioned adversary model, aim to design efficient mechanisms for dynamic data verification and operation and achieve the following goals(1)storage correctness: to ensure users that their data are indeed stored appropriately and kept intact all the time in the cloud.(2)public auditability: To allow TPA to verify the correctness of the cloud data on demand without retrieving a copy of the whole data or introducing additional online burden to the cloud users.(3) Privacy preserving: to ensure that the TPA cannot derive users data content from the information collected during the auditing process.(4) Fast localization of data error: to effectively locate the malfunctioning server when data corruption has been

4 detected. (5)Dynamic data support: to maintain the same level of storage correctness assurance even if users modify, delete, or append their data files in the cloud. (6)Batch auditing: to enable TPA with secure and efficient auditing capability to cope with multiple auditing delegations from possibly large number of different users simultaneously. (7)Lightweight: to enable users to perform storage correctness checks with minimum overhead. III. ENSURING DATA INTEGRITY FOR CLOUD 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 always be the first step to fast recover the storage errors and/or identifying potential threats of external attacks. To address these problems, 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 is needed in our scheme for file distribution across cloud servers. Then, the homomorphic token is introduced. The token computation function considered belongs to a family of universal hash function [15], chosen to preserve the homomorphic properties, which can be perfectly integrated with the verification of erasure coded data [16], [17]. Subsequently, it is shown how to derive a challenge-response protocol for verifying the storage correctness as well as identifying misbehaving servers. The procedure for file retrieval and error recovery based on erasure-correcting code is also outlined. A. File distribution File distribution deals with uploading the file onto the cloud serve. It includes the sub steps like one time user registration, block formation of the files, data block encryption and token generation. In cloud data storage, we rely on this technique to disperse the data file F redundantly across a set of n=m+ k distributed servers. For support of efficient sequential I/O to the original file, our file layout is systematic, i.e., the unmodified m data file vectors together with k parity vectors is distributed across m+k different servers. Let F =(F 1, F 2,...,F m ) and Fi = (f 1i, f 2i,...,f li ) T (i ϵ{1, m}) Here, T (shorthand for transpose) denotes that each Fi is represented as a column vector, and denotes data vector size in blocks. The block encryption can be performed by AES 256 encryption. B. File Redundancy Check In order to avoid redundant storage of the same file or files with exactly same content by different users file redundancy check is done by comparing the hash codes of the files. For a redundant file a link is provided which identifies the server where the file blocks are saved. C. Challenge Token Precomputation In order to achieve assurance of data storage correctness and data error localization simultaneously, our scheme entirely relies on the precomputed verification tokens. The main idea is as follows: before file distribution the user precomputes a certain number of short verification tokens on individual vector each token covering a random subset of data blocks G (j). 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. Upon receiving challenge, 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. Token are generated on encrypted blocks of data by homomorphic hash functions. After token generation the user stores them locally to obviate the need for encryption and lower the bandwidth overhead during dynamic data operation Algorithm 1: Token Pre computation 1. Choose parameters l, n and hash function f ; 2. Choose the number r of indices per verification;; 3. for vector G(j), j 1, n do 4. Compute v(j)=f(g(j)) 5. Store all the vi s locally. D. Integrity Check and Error Localization Proposed system not only gives a binary result of storage verification but also identifies the misbehaving server. The user reveals the challenge αi the server storing vector G (j) aggregates the blocks specified by the index and generates the signature on these rows by function f and returns the signature to TPA who verifies whether the signature matches with the pre computed tokens of the blocks. Error localization is a key prerequisite for eliminating errors in storage systems. It is also of critical importance to identify potential threats from external attacks. 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). This design can identify any number of misbehaving servers for b (m+ k) and for every challenge; each server only needs to send back Algorithm: Correctness Verification 1. Send the challenge αi to the cloud servers; 2. Receive signature R from servers 3. for (j 1, n) do 4.if (Ri (j)! = Vi (j) ) then 5. return server j is misbehaving. an aggregated value over the specified set of blocks. Thus, the bandwidth cost of this approach is much less than the straightforward approaches that require downloading all the challenged data.

5 Secure Cloud: Storage with Privacy Preserving Public Auditing and Data Dynamics E. Public Batch Auditing Cloud storage is for majority of group users. Each user may request for data security to TPA after storing data. TPA must have the ability to cope with multiple auditing delegations from large number of users. Based on FCFS principle the first request is given top priority. In this process the new authentication request will be placed in queue waiting for the first to complete. If the same users make different requests then this is not feasible. Batch Processing gathers different authentication request and process at once. PBA consists of two phases: set up and Batch verification The CSP forwards each challenge to each server. Each returns signature of challenge blocks. The organizer synthesis final response from the signature and send to TPA. The TPA verifies the response in a batch way as σ= σ i F. File Retrieval and Error Recovery The user can reconstruct the original file by downloading the data vectors from the first m servers, assuming that they return the correct response values. Whenever the data corruption is detected, the comparison of pre-computed tokens and received response values can guarantee the identification of misbehaving server(s) (with high probability).therefore, the user can always ask servers to send back blocks of the r rows specified in the challenge and regenerate the correct blocks by erasure correction, as long as the number of identified misbehaving servers is less than k. (otherwise, there is no way to recover the corrupted blocks due to lack of redundancy, even if we know the position of misbehaving servers.) The newly recovered blocks can then be redistributed to the misbehaving servers to maintain the correctness of storage. Algorithm: Error Recovery 1. Assume the block corruptions have been detected among the specified r rows; Assume s k servers have been identified misbehaving 2. Download r rows of blocks from servers; 3. Treat s servers as erasures and recover the blocks. 4. Resend the recovered blocks to corresponding servers. IV. DYNAMIC DATA OPERATIONS 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 update, delete, and append to modify the data file while maintaining the storage correctness assurance. Since data do not reside at users local site but at cloud service provider s address domain, supporting dynamic data operation can be quite challenging. On the one hand, CSP needs to process the data dynamics request without knowing the secret keying material. On the other hand, users need to ensure that all the dynamic data operation request has been faithfully processed by CSP for any data dynamic operation, the user must first generate the corresponding resulted file blocks and parities. This part of operation has to be carried out by the user, since only he knows the secret matrix P. Besides, to ensure the changes of data blocks correctly reflected in the cloud address domain, the user also needs to modify the corresponding storage verification tokens to accommodate the changes on data blocks. Only with the accordingly changed storage verification tokens, the previously discussed challengeresponse protocol can be carried on successfully even after data dynamics. In other words, these verification tokens help to ensure that CSP would correctly execute the processing of any dynamic data operation request. Otherwise, CSP would be caught cheating with high probability in the protocol execution later on. Given this design methodology, the straightforward and trivial way to support these operations is for user to download all the data from the cloud servers and recomputed the whole parity blocks as well as verification tokens. This would clearly be highly inefficient.the challenge response protocol could be carried out successfully if user modifies the corresponding verification tokens. A. Update Operation In cloud data storage, sometimes the user may need to modify some data block(s) stored in the cloud, we refer this operation as data update. In other words, for all the unused tokens, the user needs to exclude every occurrence of the old data block and replace it with the new one. User may need to modify some data blocks stored in the cloud from its current value f ij to a new one, f ij +Δf ij. The update matrix ΔF= (ΔF1, ΔF2 ΔFm). User multiplies ΔF by A and thus generates update information for both the data vectors and parity vectors.user can perform the update operation and generate the updated parity blocks by using Δf ij only, without involving any other unchanged blocks. Specifically, the user can construct a general update matrix ΔF with zero elements in ΔF to denote the unchanged blocks and thus ΔF should only be a sparse matrix most of the time. Because the data update operation inevitably affects some or all of the remaining verification tokens, after preparation of update information, the user has to amend those unused tokens for each vector G (j) to maintain the same storage correctness assurance. In other words, for all the unused tokens, the user needs to exclude every occurrence of the old data block and replace it with the new one. The user sends the update information to the cloud servers which perform the update operation as G (j) =G (j) +ΔG (j) ; (jϵ{1, n}) B. Delete Operation Sometimes, after being stored in the cloud, certain data blocks may need to be deleted. The delete 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 delete operation is actually a special case of the data update operation, where the original data blocks can be replaced with zeros or some predetermined special blocks. It is a special case of update operation thus we can have Δf ij in ΔF to be - Δf ij. Also the affected tokens need to be modified.

6 C. Append Operation In some cases, 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. To increase the size of stored data by adding blocks at the end of the data file. Initially there are l rows in the file matrix if user wants to append m blocks at the end of the file as (f l+1,1 ; f l+1,2 ;... ; f l+1,m) V. RELATED WORK Juels and Kaliski Jr. [10] described a formal proof of retrievability (POR) model for ensuring the remote data integrity. Their scheme combines spot-checking and error correcting code to ensure both possession and retrievability of files on archive service systems. Shacham and Waters [17] built on this model and constructed a random linear function based homomorphic authenticator which enables unlimited number of challenges and requires less communication overhead due to its usage of relatively small size of BLS signature. Bowers et al. [18] proposed an improved framework for POR protocols that generalizes both Juels and Shacham s work. Later in their subsequent work, Bowers et al. [13] extended POR model to distributed systems. However, all these schemes are focusing on static data. The effectiveness of their schemes rests primarily on the preprocessing steps that the user conducts before outsourcing the data file F. Any change to the contents of F, even few bits, must propagate through the error correcting code and the corresponding random shuffling process, thus introducing significant computation and communication complexity. Recently, Dodis et al. [10] gave theoretical studies on generalized framework for different variants of existing POR work. Ateniese et al. [11] defined the provable data possession (PDP) model for ensuring possession of file on untrusted storages. Their scheme utilized public key-based homomorphic tags for auditing the data file. However, the pre-computation of the tags imposes heavy computation overhead that can be expensive for an entire file. In their subsequent work, Ateniese et al. [14] described a PDP scheme that uses only symmetric key-based cryptography. This method has lower overhead than their previous scheme and allows for block updates, deletions, and appends to the stored file, which has also been supported in my work. However, their scheme focuses on single server scenario and does not provide data availability guarantee against server failures, leaving both the distributed scenario and data error recovery issue unexplored. The explicit support of data dynamics has further been studied in the two recent works [15] and [16]. Wang et al. [15] proposed to combine BLS-based homomorphic authenticator with Merkle Hash Tree to support fully data dynamics, while Erway et al. [16] developed a skip list-based scheme to enable provable data possession with fully dynamics support. The incremental cryptography work done by Bellare et al. [36] also provides a set of cryptographic building blocks such as hash, MAC, and signature functions that may be employed for storage integrity verification while supporting dynamic operations on data. However, this branch of work falls into the traditional data integrity protection mechanism, where local copy of data has to be maintained for the verification. It is not yet clear how the work can be adapted to cloud storage scenario where users no longer have the data at local sites but still need to ensure the storage correctness efficiently in the cloud. VI. CONCLUSION The problem of data security in cloud data storage is being investigated, which is essentially a distributed storage system. ie, Propose privacy preserving public auditing system that support data dynamics for data storage security in cloud. To achieve the assurances of cloud data integrity and availability and enforce the quality of dependable cloud storage service for users, my work propose an effective and flexible distributed scheme with explicit dynamic data support, including block update, delete, and append. By utilizing the homomorphic token with distributed verification of erasure coded data, proposed scheme is expected to achieve the integration of storage correctness insurance and data error localization, i.e., whenever data corruption has been detected during the storage correctness verification across the distributed servers, it almost guarantee the simultaneous identification of the misbehaving server(s). Homomorphic authenticator guarantees that TPA will not learn the user content which eliminates the user from the burden of auditing task. Considering the time, computation resources, and even the related online burden of users, proposed system is expected to be highly efficient and secure Homomorphic linear authenticator is utilized to check integrity and random masking to guarantee that the TPA would not learn any knowledge about the data content stored. The full-fledged implementation of the mechanism on commercial public cloud is an important future extension, which is expected to robustly cope with very large scale data and thus encourage users to adopt cloud storage services more confidently. Design could be implemented for cloud storage of an organisation where the administrator provides access credentials to various personnel of the organisation who can concurrently access the data privileged to them with increased rate of security and integrity. VII. REFERENCES [1] C. Wang, Q. Wang, K. Ren, and W. Lou, Towards Secure and expandable Storage Services in Cloud Computing, Proc. 17th Int l Workshop Quality of Service, pp. 1-9, July [2] C. Wang, Q. Wang, K. Ren, and W. Lou, Ensuring Data Storage Security in Cloud Computing, Proc. 17th Int l Workshop Quality of Service, pp. 1-9, July [3] Amazon.com, Amazon Web Services (AWS), amazon.com, 2009.

7 Secure Cloud: Storage with Privacy Preserving Public Auditing and Data Dynamics [4] Sun Microsystems, Inc., Building Customer Trust in Cloud Computing with Transparent Security, Nov [5] K. Ren, C. Wang, and Q. Wang, Security Challenges for the Public Cloud, IEEE Internet Computing, vol. 16, no. 1, pp , [6] K.D. Bowers, A. Juels, and A. Oprea, Proofs of Retrievability: Theory and Implementation, Proc. ACM Workshop Cloud Computing Security (CCSW 09), [7] C. Wang, S.S.M. Chow, Q. Wang, K. Ren, and W. Lou, Privacy- Preserving Public Auditing for Secure Cloud Storage, IEEE Trans. Computers, preprint, 2012, [8] K.D. Bowers, A. Juels, and A. Oprea, HAIL: A High- Availability and Integrity Layer for Cloud Storage, Proc. ACM Conf. Computer and Comm. Security (CCS 09), pp , [ 9]Ashish Bhagat, Ravi Kant Sahu., Using Third Party Auditor for Cloud Data Security: A Review International Journal of Advanced Research in Computer Science and Software Engineering. [10] Akkala.Saibabu, T.Satyanarayana Murthy Security Provision in Publicly Auditable Secure Cloud Data Storage Services Using SHA-1 Algorithm International Journal of Computer Science and Information Technologies, Vol. 3 (3), 2012, [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]. Q. Wang, C. Wang, J. Li, K. Ren, and W. Lou, Enabling Public Verifiability and Data Dynamics for Storage Security [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 Int l Conf. Security and Privacy in Comm. Networks (SecureComm 08), pp. 1-10, [15] L. Carter and M. Wegman, Universal Hash Functions, J.Computer and System Sciences, vol. 18, no. 2, pp , [16] J. Hendricks, G. Ganger, and M. Reiter, Verifying Distributed Erasure-Coded Data, Proc. 26th ACM Symp. Principles ofdistributed Computing, pp , [17] T. Schwarz and E.L. Miller, Store, Forget, and Check: Using Algebraic Signatures to Check Remotely Administered Storage, Proc. IEEE Int l Conf. Distributed Computing Systems (ICDCS 06),pp , 2006.

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