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

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 (tolerance to typos and variants in query keywords), 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 VeriFuzzy, a novel DVFS service framework that cohesively integrates three innovations: an Enhanced Virtual Binary Tree (EVBTree) that decouples fuzzy semantics from index logic to support O(\log n) search/updates; a blockchain-reconstructed verification mechanism that ensures result integrity with logarithmic complexity; and a 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 × 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.