VeriFuzzy: A Dynamic Verifiable Fuzzy Search Service for Encrypted Cloud Data

• URL: http://arxiv.org/abs/2507.10927v2
• Date: Sun, 28 Sep 2025 15:45:47 GMT
• Title: VeriFuzzy: A Dynamic Verifiable Fuzzy Search Service for Encrypted Cloud Data
• Authors: Jie Zhang, Xiaohong Li, Man Zheng, Ruitao Feng, Shanshan Xu, Zhe Hou, Guangdong Bai,
• Abstract summary: Service that supports dynamic, verifiable fuzzy search (DVFS) over encrypted cloud data remains a fundamental challenge.<n>This paper presents textbfVeriFuzzy, a novel DVFS service framework that cohesively integrates three innovations.<n>Our code and dataset are now open source, hoping to inspire future DVFS research.
• Score: 13.863905835870836
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Enabling search over encrypted cloud data is essential for privacy-preserving data outsourcing. While searchable encryption has evolved to support individual requirements like fuzzy matching, dynamic updates, and result verification, designing a service that supports dynamic, verifiable fuzzy search (DVFS) over encrypted cloud data remains a fundamental challenge due to inherent conflicts between underlying technologies. Existing approaches struggle with simultaneously achieving efficiency, functionality, and security, often forcing impractical trade-offs. This paper presents \textbf{VeriFuzzy}, a novel DVFS service framework that cohesively integrates three innovations: an \textit{Enhanced Virtual Binary Tree (EVBTree)} that decouples fuzzy semantics from index logic to support $O(\log n)$ search/updates; a \textit{blockchain-reconstructed verification} mechanism that ensures result integrity with logarithmic complexity; and a \textit{dual-repository state management} scheme that achieves IND-CKA2 security by neutralizing branch leakage. Extensive evaluation on 3,500+ documents shows VeriFuzzy achieves 41\% faster search, $5\times$ more efficient verification, and constant-time index updates compared to state-of-the-art alternatives. Our code and dataset are now open source, hoping to inspire future DVFS research.

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