Blockchain-Assisted Privacy-Preserving Retrieval for Sensitive Outsourced Data

Abstract

With the continuous outsourcing of sensitive data to cloud platforms and third-party environments, how to achieve efficient and trustworthy data retrieval while preserving data confidentiality and query privacy has become a critical issue in outsourced data security management. Existing studies mainly focus on secure storage and privacy-preserving retrieval but suffer from limitations in the efficiency of multi-attribute conjunctive queries, the suppression of intermediate information leakage during auxiliary condition verification, and the trustworthy traceability of the retrieval process. To address these issues, this paper proposes a blockchain-assisted privacy-preserving retrieval framework for sensitive outsourced data. The framework adopts a collaborative paradigm of off-chain storage and retrieval together with on-chain commitment and auditing. By combining a frequency-aware primary search term selection strategy with a concealed auxiliary verification mechanism, it enables secure filtering of task-relevant data under multi-attribute conditions. Meanwhile, by recording index commitments, query digests, and result digests on the blockchain, the proposed framework enhances the verifiability and traceability of index states and access processes. Security analysis and experimental results demonstrate that the proposed scheme can achieve more efficient construction of auxiliary decision structures, more stable query performance, and better storage overhead while preserving data confidentiality and query privacy.