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

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 $\mathbf{\text{VeriFuzzy}}$, a novel DVFS service framework that cohesively integrates three innovations: an $\mathit{\text{Enhanced}}\mathit{\text{Virtual}}\mathit{\text{Binary}}\mathit{\text{Tree}}\mathit{\text{(EVBTree)}}$ that decouples fuzzy semantics from index logic to support $O(\log n)$ search/updates; a $\mathit{\text{blockchain-reconstructed}}\mathit{\text{verification}}$ mechanism that ensures result integrity with logarithmic complexity; and a $\mathit{\text{dual-repository}}\mathit{\text{state}}\mathit{\text{management}}$ 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.