Privacy-Preserving Data Auditing with Efficient Corrupted Data Locating for Cloud Computing

Cloud computing enables on-demand access to a shared pool for computing, storage, networks, services, and applications. As an efficient provable data possession method, data auditing protocol allows a client to verify the integrity of the stored data on an untrusted server. Whereas, with the increasing threat of cyber security, data auditing protocols face increasing risks of privacy leakage. In particular, the challenge requests from the auditor to server may expose the addresses of the outsourced data, thereby threatening the security of the stored data. On the other hand, efficient corrupted data locating has always been the main bottleneck that restricts the performance of data auditing protocols. In this paper, we design a privacy-preserving data auditing protocol with efficient corrupted data locating. First, the indistinguishability obfuscation (iO) technique is introduced in the sampling process thus protecting the addresses of sensitive data. Second, an efficient corrupted data locating method is designed via the binary search algorithm, which could locate the corrupted data in logarithmic complexity. Third, memory factors are introduced to adaptively adjust the sampling range, thereby facilitating auditing with higher accuracy. Finally, we conduct a performance evaluation to prove the privacy-preserving, corruption locating, and adaptive sampling selection properties of the proposed protocol. The evaluation results show that the proposed protocol is superior to relevant existing works.