Releasing Private Data for Numerical Queries
Yuan Ying Qiu, Wei Dong, Ke Yi, Bin Wu, Feifei Li · Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining · 2022
Prior work on private data release has only studied counting queries or linear queries, where each tuple in the dataset contributes a value in [0,1] and a query returns the sum of the values. However, many data analytical tasks involve numerical values that are arbitrary real numbers. In this paper, we present a new mechanism to privatize a dataset D for a given set Q of numerical queries, achieving an error of Õ (√n • Δw(D)) for each query w ∈ Q, where Δw(D) is the maximum contribution of any tuple in D queried by w. This instance- and query-specific error bound not only is theoretically appealing, but also leads to excellent practical performance.