Abstract. Cloud Computing has been envisioned as the next-generation architecture of IT Enterprise. It moves the application software and databases to the centralized large data centers, where the management of the data and services may not be fully trustworthy. This unique paradigm brings about many new security challenges, which have not been well understood. This work studies the problem of ensuring the integrity of data storage in Cloud Computing. In particular, we consider the task of allowing a third party auditor (TPA), on behalf of the cloud client, to verify the integrity of the dynamic data stored in the cloud. The introduction of TPA eliminates the involvement of client through the auditing of whether his data stored in the cloud is indeed intact, which can be important in achieving economies of scale for Cloud Computing. The support for data dynamics via the most general forms of data operation, such as block modification, insertion and deletion, is also a significant step toward practicality, since services in Cloud Computing are not limited to archive or backup data only. While prior works on ensuring remote data integrity often lacks the support of either public verifiability or dynamic data operations, this paper achieves both. We first identify the difficulties and potential security problems of direct extensions with fully dynamic data updates from prior works and then show how to construct an elegant verification scheme for seamless integration of these two salient features in our protocol design. In particular, to achieve efficient data dynamics, we improve the Proof of Retrievability model [1] by manipulating the classic Merkle Hash Tree (MHT) construction for block tag authentication. Extensive security and performance analysis show that the proposed scheme is highly efficient and provably secure. 1

Abstract—Remote data integrity checking is a crucial technology in cloud computing. Recently many works focus on providing data dynamics and/or public verifiability to this type of protocols. Existing protocols can support both features with the help of a third party auditor. In a previous work, Sebé et al. [1] propose a remote data integrity checking protocol that supports data dynamics. In this paper, we adapt Sebé et al.’s protocol to support public verifiability. The proposed protocol supports public verifiability without help of a third party auditor. In addition, the proposed protocol does not leak any private information to third party verifiers. Through a formal analysis, we show the correctness and security of the protocol. After that, through theoretical analysis and experimental results, we demonstrate that the proposed protocol has a good performance.

Cloud computing provides customers the illusion of infinite computing resources which are available from anywhere, anytime, on demand. Computing at such an immense scale requires a framework that can support extremely large datasets housed on clusters of commodity hardware. Two examples of such frameworks are Google’s MapReduce and Microsoft’s Dryad. First we discuss implementation details of these frameworks and drawbacks where future work is required. Next we discuss the challenges of computing at such a large scale. In particular, we focus on the security issues which arise in the cloud: the confidentiality of data, the retrievability and availability of data, and issues surrounding the correctness and confidentiality of computation executing on third party hardware. 1.

We introduce a model for provable data possession (PDP) that can be used for remote data checking: A client that has stored data at an untrusted server can verify that the server possesses the original data without retrieving it. The model generates probabilistic proofs of possession by sampling random sets of blocks from the server, which drastically reduces I/O costs. The client maintains a constant amount of metadata to verify the proof. The challenge/response protocol transmits a small, constant amount of data, which minimizes network communication. Thus, the PDP model for remote data checking is lightweight and supports large data sets in distributed storage systems. The model is also robust in that it incorporates mechanisms for mitigating arbitrary amounts of data corruption. We present two provably-secure PDP schemes that are more efficient than previous solutions. In particular, the overhead at the server is low (or even constant), as opposed to linear in the size of the data. We then propose a generic transformation that adds robustness to any remote data checking scheme based on spot checking. Experiments using our implementation verify the practicality of PDP and reveal that the performance of PDP is bounded by disk I/O and not by cryptographic computation. Finally, we conduct an in-depth experimental evaluation to study the tradeoffs in performance, security, and space overheads when

Currently, the amount of sensitive data produced by many organizations is outpacing their storage ability. The management of such huge amount of data is quite expensive due to the requirements of high storage capacity and qualified personnel. Storage-as-a-Service (SaaS) offered by cloud service providers (CSPs) is a paid facility that enables organizations to outsource their data to be stored on remote servers. Thus, SaaS reduces the maintenance cost and mitigates the burden of large local data storage at the organization’s end. A data owner pays for a desired level of security and must get some compensation in case of any misbehavior committed by the CSP. On the other hand, the CSP needs a protection from any false accusation that may be claimed by the owner to get illegal compensations. In this paper, we propose a cloud-based storage scheme that allows the data owner to benefit from the facilities offered by the CSP and enables indirect mutual trust between them. The proposed scheme has four important features: (i) it allows the owner to outsource sensitive data to a CSP, and perform full block-level dynamic operations on the outsourced data, i.e., block modification, insertion, deletion, and append, (ii) it ensures that authorized users (i.e., those who have the right to access the owner’s file) receive the latest version of the outsourced data, (iii) it enables indirect mutual trust between the owner and the CSP, and (iv) it allows the owner to grant or revoke access to the outsourced data. We discuss the security issues of the proposed scheme. Besides, we justify its performance through theoretical analysis and experimental evaluation of storage, communication, and computation overheads.

Currently, many individuals and organizations outsource their data to remote cloud service providers (CSPs) seeking to reduce the maintenance cost and the burden of large local data storage. The CSP offers paid storage space on its infrastructure to store customers ’ data. Replicating data on multiple servers across multiple data centers achieves a higher level of scalability, availability, and durability. The more copies the CSP is asked to store, the more fees the customers are charged. Therefore, customers need to be strongly convinced that the CSP is storing all data copies that are agreed upon in the service contract, and the data-update requests issued by the customers have been correctly executed on all remotely stored copies. In this paper we propose two dynamic multi-copy provable data possession schemes that achieve two main goals: i) they prevent the CSP from cheating and using less storage by maintaining fewer copies, and ii) they support dynamic behavior of data copies over cloud servers via operations such as block modification, insertion, deletion, and append. We prove the security of the proposed schemes against colluding servers. Through theoretical analysis and experimental results, we demonstrate the performance of these schemes. Additionally, we discuss how to identify corrupted copies by slightly modifying the proposed schemes.

�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. Though the benefits are clear, such a service is also relinquishing users ’physical possession of their outsourced data, which inevitably poses new security risks towards the correctness of the data in cloud. In order to address this new problem and further achieve a secure and dependable cloud storage service, I propose in this paper a flexible distributed storage integrity auditing mechanism, utilizing the homomorphism token 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 ensures strong cloud storage correctness guarantee, but also simultaneously achieves fast data error localization, i.e., the Identification of misbehaving server. Considering the cloud data are dynamic in nature, the proposed design further supports secure and efficient dynamic operations on outsourced data, including block modification, deletion, and append. Analysis shows the proposed scheme is highly efficient and resilient against Byzantine failure, malicious data modification attack, and even server colluding attacks. 1.

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. Though the benefits are clear, such a service is also relinquishing users ’ physical possession of their outsourced data, which inevitably poses new security risks towards the correctness of the data in cloud. In order to address this new problem and further achieve a secure and dependable cloud storage service, we propose in this paper a flexible distributed storage integrity auditing mechanism, utilizing the homomorphic token 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 ensures strong cloud storage correctness guarantee, but also simultaneously achieves fast data error localization, i.e., the identification of misbehaving server. Considering the cloud data are dynamic in nature, the proposed design further supports secure and efficient dynamic operations on outsourced data, including block modification, deletion, and append. Analysis shows the proposed scheme is highly efficient and resilient against Byzantine failure, malicious data modification attack, and even server colluding attacks 1

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