Random Chunks Generation Attack Resistant Cross-User Deduplication for Cloud Storage
Xin Lai Tang, Yiteng Zhou, Yudan Zhu, Mingjun Fu, Luchao Jin · 2023
Side channel attack is a serious threat in cross-user deduplication, which could be utilized to steal existence privacy of the target data in cloud storage in a statistical way. Even though dirty chunks processing is employed as an effective technique to deal with this risk, it brings about the problem of low efficiency since every entailed chunk is required. Moreover, in this paper, we propose a more sophisticated random chunks generation attack, which still cannot be well resisted since non-duplicate chunks in requests could be constructed artificially to avoid being added into the dirty chunk list. In order to resist this attack, we propose a novel secure deduplication mechanism for cloud storage, which takes the lead to well protect the existence privacy of target chunks. Specifically, we design a mixed security strategy to construct the response table according to the number of non-duplicate chunks adaptively, which ensures the security with the minimum cost introduced no matter how many randomly generated non-duplicate chunks are contained in a single deduplication request. Furthermore, we also propose a lightweight dirty chunks processing mechanism to achieve security under statistical side channel attacks. The security analysis and experimental results show that the proposed scheme is able to resist the random chunks generation attack in a lightweight way comparing with the state-of-the-art.