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

2025-07-14

AI Summary

In cloud storage, encrypted fuzzy multi-keyword search faces a fundamental trade-off between security and efficiency: linear search ensures adaptive security but incurs high overhead, while tree-based indexes improve performance yet leak branching information. Method: This paper proposes a dynamic verifiable fuzzy search scheme that (i) constructs a leakage-resilient index by integrating locality-sensitive hashing with a virtual binary tree; (ii) employs a dual-database versioning mechanism to achieve forward privacy and efficient dynamic updates; and (iii) leverages blockchain smart contracts to enable verifiable correctness and completeness of search results. Contribution/Results: The proposed scheme reduces both search and verification complexity to O(log n). It achieves strong security guarantees—including no branch leakage, adaptive security, and forward privacy—while significantly enhancing retrieval efficiency and scalability over large-scale datasets.

Technology Category

• Search and Optimization: Distributed Search
• Data Mining & Knowledge Management: Intelligent Query Processing
• Machine Learning: Privacy

Application Category

• Security and Privacy: Data transparency and provenance
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

Cloud storage introduces critical privacy challenges for encrypted data retrieval, where fuzzy multi-keyword search enables approximate matching while preserving data confidentiality. Existing solutions face fundamental trade-offs between security and efficiency: linear-search mechanisms provide adaptive security but incur prohibitive overhead for large-scale data, while tree-based indexes improve performance at the cost of branch leakage vulnerabilities. To address these limitations, we propose DVFS - a dynamic verifiable fuzzy search service with three core innovations: (1) An extit{adaptive-secure fuzzy search} method integrating locality-sensitive hashing with virtual binary trees, eliminating branch leakage while reducing search complexity from linear to sublinear ($O(log n)$ time); (2) A extit{dual-repository version control} mechanism supporting dynamic updates with forward privacy, preventing information leakage during operations; (3) A extit{blockchain-based verification system} that ensures correctness and completeness via smart contracts, achieving $O(log n)$ verification complexity. Our solution advances secure encrypted retrieval by simultaneously resolving the security-performance paradox and enabling trustworthy dynamic operations.

Problem

• Secure fuzzy multi-keyword search for encrypted cloud data
• Eliminate branch leakage in tree-based search indexes
• Dynamic updates with forward privacy and verifiable correctness

Innovation

• Adaptive-secure fuzzy search with sublinear complexity
• Dual-repository version control for dynamic updates
• Blockchain-based verification via smart contracts