DEFD: Dual-Entity Fuzzy Deduplication for Untrusted Environments
Zehui Tang, Shengke Zeng, Song Han, Qi Yu, Shihai Jiang, Peng Chen · 2024
Fuzzy deduplication frees up more storage space than exact deduplication. Traditional deduplication schemes do not only fail to extend to fuzzy deduplication directly but also be vulnerable to brute-force guessing attacks (e.g. Convergent Encryption (CE) and Message-locked Encryption (MLE)). Currently, fuzzy deduplication is mainly handled with the help of aided-server and a third-party validator for the security and feasibility. This work extracts a single-server fuzzy deduplication scheme for encrypted multimedia data under dual-entity (e.g., uploader & server, and a third-party is not necessary). In addition, we perform experimental evaluation of DEFD on real-world datasets. The results show that DEFD can save 95+% of storage space, and for the real outsourced data deduplication scenarios DEFD can improve the accuracy by 5.287%.