An Efficient and Secure Anonymous Query Protocol
Wei Hu, Li Yin · 2024
Nowadays, the proliferation of data-driven applications has intensified the challenge of balancing data utility with user privacy protection. Existing privacy-preserving query schemes fail to meet the requirements of practical applications due to their limitations in execution efficiency and communication overhead. This paper proposed a novel secure anonymous query protocol to address these limitations. Our approach uniquely combines data partitioning with a modified Oblivious Transfer (OT) protocol for query data preprocessing, obliviously reducing the query range and enhancing efficiency. We further integrate additive secret sharing with Garbled Bloom Filters to strengthen query privacy. A thorough security analysis conducted under the semi-honest model has validated the protocol’s effectiveness in protecting client and data privacy. Experimental evaluations show that our protocol execution time is reduced by up to 69% and communication overhead is reduced by 49% for a dataset containing 106records compared to existing methods.