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Blockchain-based public auditing and secure deduplication with fair arbitration

机译:基于区块链的公平审计和公平仲裁的安全重复数据删除

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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. (C) 2020 Elsevier Inc. All rights reserved.
机译:数据审核使数据所有者能够验证其敏感数据的完整性,而不会检索它们。此功能已被商业云存储广泛采用。但是,现有方法仍然有一些缺点。一方面,现有计划具有公平仲裁的缺陷,即现有审计计划缺乏惩罚恶意云服务提供商(CSP)的有效方法,并补偿数据完整性被销毁的用户。另一方面,CSP可以存储冗余和重复数据。这些冗余数据在整个数据生命周期中不可避免地增加管理开销和计算成本。为解决这些挑战,我们提出了一个基于区块链的公开审计和安全的重复数据删除计划,具有公平仲裁。通过使用智能合同,我们的计划支持自动惩罚恶意CSP,并补偿数据完整性损坏的用户。此外,我们的方案介绍了锁定的加密算法,并在数据审核中删除随机掩蔽。与现有方案相比,我们的方案可以有效地降低标签验证和数据存储成本的计算成本。我们提供全面的分析,以展示在储存,批量审计和数据一致性方面的建议方案的正确性。此外,在Ethereum Blockchain平台上进行的广泛实验表明了我们计划的效率和有效性。 (c)2020 Elsevier Inc.保留所有权利。

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