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

TL;DR

VeriFuzzy is a novel framework enabling efficient, secure, and verifiable fuzzy search over encrypted cloud data, addressing key challenges in privacy-preserving data retrieval with innovative index and verification mechanisms.

Contribution

It introduces a cohesive DVFS service with an enhanced index structure, blockchain-based verification, and secure state management, advancing the efficiency and security of fuzzy search over encrypted data.

Findings

41% faster search performance

5x more efficient verification process

Constant-time index updates

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.