Files and links (2)
Preprint (Author's original)CC BY-NC-ND V4.0, Open Access
Preprint (Author's original)CC BY-NC-ND V4.0, Open
Preprint
VeriFuzzy: A Dynamic Verifiable Fuzzy Search Service for Encrypted Cloud Data
arXiv (Cornell University), (8)
Cornell University
15/07/2025
Metrics
1 Record Views
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 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, 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.
Details
- Title
- VeriFuzzy: A Dynamic Verifiable Fuzzy Search Service for Encrypted Cloud Data
- Creators
- Jie Zhang - Tianjin UniversityXiaohong Li - Tianjin UniversityMan Zheng - Xiong’an New Area Power Supply Company (China)Ruitao Feng - Southern Cross UniversityShanshan Xu - East China Normal UniversityZhe Hou - Griffith UniversityGuangdong Bai - City University of Hong Kong
- Publication Details
- arXiv (Cornell University), (8)
- Publisher
- Cornell University
- Identifiers
- 991013296651902368
- Academic Unit
- Faculty of Science and Engineering
- Language
- English
- Resource Type
- Preprint