Publication Type

Journal Article

Version

acceptedVersion

Publication Date

12-2020

Abstract

Data auditing enables data owners to verify the integrity of their sensitive data stored at an untrusted cloud without retrieving them. This feature has been widely adopted by commercial cloud storage. However, the existing approaches still have some drawbacks. On the one hand, the existing schemes have a defect of fair arbitration, i.e., existing auditing schemes lack an effective method to punish the malicious cloud service provider (CSP) and compensate users whose data integrity is destroyed. On the other hand, a CSP may store redundant and repetitive data. These redundant data inevitably increase management overhead and computational cost during the whole data life cycle. To address these challenges, we propose a blockchain-based public auditing and secure deduplication scheme with fair arbitration. By using a smart contract, our scheme supports automatic penalization of the malicious CSP and compensates users whose data integrity is damaged. Moreover, our scheme introduces a message-locked encryption algorithm and removes the random masking in data auditing. Compared with the existing schemes, our scheme can effectively reduce the computational cost of tag verification and data storage costs. We give a comprehensive analysis to demonstrate the correctness of the proposed scheme in terms of storage, batch auditing, and data consistency. Also, extensive experiments conducted on the platform of Ethereum blockchain demonstrate the efficiency and effectiveness of our scheme.

Keywords

Blockchain, Data auditing, Fair arbitration, Data deduplication

Discipline

Databases and Information Systems | Finance and Financial Management | Technology and Innovation

Research Areas

Data Science and Engineering

Publication

Information Sciences

Volume

541

First Page

409

Last Page

425

ISSN

0020-0255

Identifier

10.1016/j.ins.2020.07.005

Publisher

Elsevier

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1016/j.ins.2020.07.005

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