Enabling Cross-Clouds Encrypted Data Fine-Grained Deduplication and Recovery via Blockchain Systems
Bingyun Liu, Xiaojun Zhang, Juncai Chen, Yinbin Miao, Jie Zhao, Huaxiong Wang · IEEE Transactions on Dependable and Secure Computing · 2026
Data deduplication contributes to avoiding great waste of cloud storage resources. Most of current data deduplication schemes focus on file-level data deduplication, without realizing deduplication ownership privacy protection, data blocks retrieval or error data blocks recovery. To this end, this paper proposes cross-clouds encrypted data fine-grained deduplication and recovery (CC-FGDR) scheme supporting blockchain collaborative computing. CC-FGDR exploits two-party joint generation of deduplication tags to prevent deduplication privacy leakage, and constructs vector-commitment ciphertext tree to realize proofs of ownership verification of distributed backup data blocks via blockchain systems. CC-FGDR achieves secure cross-clouds deduplication detection by constructing twin bloom filter tree and privacy puncture pseudo-random function. To achieve data availability, a ciphertext reconstruction matrix is introduced and combined with countable bloom filter to achieve the location, search and retrieval of distributed outsourced data blocks. CC-FGDR further designs low-density parity check code to achieve cross-clouds encrypted error data recovery through collaborative computing of smart contracts. The security analysis demonstrates CC-FGDR ensures data storage verifiability, data confidentiality, and ownership privacy protection. Performance evaluation demonstrates the feasibility of CC-FGDR in the deployment of multiple clouds via blockchain systems, and CC-FGDR significantly improves the deduplication efficiency by joint deduplication in multi-clouds.