Distributed privacy-preserving keyword querying for integrated data in IoT networks via function secret sharing

Abstract

The growing adoption of IoT applications underscores the need for advanced data fusion and information acquisition techniques, driving demand for secure, privacy-preserving querying of integrated IoT data. Existing schemes like searchable encryption are practical but leak access patterns, while leakage-free methods using Oblivious RAM or cryptographic techniques incur significant resource overhead. In this paper, we propose PQBL, a framework for privacy-preserving, trusted data integration and search, leveraging distributed trust against malicious attackers. Our query scheme combines function secret sharing and blockchain to enable efficient, privacy-preserving searches on encrypted IoT data. To improve search efficiency, we introduce a compressed RAMBO Bloom Filter for keyword trapdoors. Formal security analysis shows that PQBL leaks no search patterns and is secure against Privacy under Selective Chosen-Plaintext Attacks. Extensive experiments on the PQBL prototype validate its effectiveness and efficiency.