A Privacy-Preserving Data Query Framework for Blockchain-based Information Systems: Integrating CP-ABE and PIR
Zhaozhong Guo, Meipeng Li, Peng Zhang, Yichen Li · 2025
Current information systems face significant limitations across multiple dimensions, including challenges in ensuring data authenticity, inadequate security in resource-sharing mechanisms, and persistent difficulties in safeguarding data privacy. When blockchain technology is employed in information systems for data storage and querying, its inherent transparency and decentralization introduce risks of privacy breaches. To address this issue, we propose a novel privacy-preserving data query framework that integrates on-chain/off-chain storage architecture and ciphertext-policy attribute-based encryption (CP-ABE) for fine-grained access control, and private information retrieval (PIR) for data querying. This framework ensures the confidentiality of queried data, anonymizes querying entities, and safeguards the privacy of raw datasets. Theoretical analysis confirms the system's correctness while rigorously validating the privacy preservation of both querying parties and original data.