Abstract：The encrypted query integrity authentication mechanism can provide assurance for the reliability of the query results while protecting the data privacy of artificial intelligence applications. However, the existing encrypted range query integrity authentication methods suffer from high overhead in authentication data structure construction and poor data scalability. To address these issues, the causes of performance bottlenecks in secure verifiable and efficient framework(ServeDB)are analyzed. Based on the analysis conclusions, a cube-cell-based authentication method(CubeTree) is proposed for the encrypted range query integrity authentication problem. A quantile-normalization-based data redistribution optimization is adopted to balance the data distribution in the domain. The encoding overheads of data records are reduced by the data redistribution optimization. Furthermore, a flat balanced K-ary tree structure and a cube-cell-based index authentication data structure are proposed. The redundancy of the authentication data structure is significantly reduced by merging data records with same codes and adopting cube cells as the basic units. Consequently, the computational and storage costs of the CubeTree construction are decreased. Experiments on real-world and synthetic datasets show that CubeTree can significantly reduce the construction costs of the authentication data structure and the generation/verification costs of query integrity proofs, while efficiently handling large-scale datasets with hundreds of millions of data records.
王肇康, 潘佳辉, 周璐. 支持亿级数据的高效密文范围查询完整性验证[J]. 模式识别与人工智能, 2024, 37(1): 27-46.
WANG Zhaokang, PAN Jiahui, ZHOU Lu. Efficient Encrypted Range Query Integrity Authentication for Hundreds of Millions of Records. Pattern Recognition and Artificial Intelligence, 2024, 37(1): 27-46.
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