Sec-Reduce: Secure Reduction of Redundant and Similar Data for Cloud Storage based on Zero-Knowledge Proof

Abstract

With the widespread adoption of cloud storage, effectively identifying and eliminating redundant data among users while ensuring data security has become a significant challenge. However, traditional similarity detection methods has limitations in privacy protection. Although conventional encryption techniques can safeguard privacy, they have difficulty detecting redundancy between similar blocks. Thus, we propose a secure reduction of redundant and similar data for cloud storage to address these challenges based on zero-knowledge proof (Sec-Reduce), called Sec-Reduce. It first employs a novel zero-knowledge proof technique for file-level redundancy detection, where redundant files are identified and excluded from storage. To further determine the similarity of non-redundant files, the scheme performs content-based chunking and feature extraction using a similarity feature extraction method. These extracted features are then encrypted using the approximate homomorphic encryption scheme Cheon-Kim-Kim-Song (CKKS) to enable similarity detection in the ciphertext environment. Finally, secure delta encoding is applied to store unique ciphertext blocks and deltas. Evaluations of real-world datasets demonstrate that Sec-Reduce achieves higher storage savings than existing encrypted storage methods, with storage overhead comparable to plaintext storage and only moderate performance overhead.