Partition selection, or set union, is an important primitive in differentially private mechanism design: in a database where each user contributes a list of items, the goal is to publish as many of these items as possible under differential privacy. In this work, we present a novel mechanism for differentially private partition selection. This mechanism, which we call DP-SIPS, is very simple: it consists of iterating the naive algorithm over the data set multiple times, removing the released partitions from the data set while increasing the privacy budget at each step. This approach preserves the scalability benefits of the naive mechanism, yet its utility compares favorably to more complex approaches developed in prior work. Along the way, this work also gives an alternate definition of approximate zero-concentrated DP, and reports some empirical observations on the utility of other partition selection mechanisms.
翻译:分区选择, 或设定组合, 在差别化的私人机制设计中是一个重要的原始点: 在每个用户都提供项目清单的数据库中, 目标是在有差别的隐私下尽可能多地公布这些项目。 在这项工作中, 我们提出了一个区分私人分区选择的新机制。 我们称之为DP- SIPS 的这个机制非常简单: 它包含多次重复天真的算法, 多次重复数据集, 从数据集中去除释放的分区, 同时增加每个步骤的隐私预算。 这个方法保留天真的机制的可伸缩性好处, 但它的实用性可以比先前工作中开发的更复杂的方法更好。 顺便说一下, 这项工作还给出了大约零集中的DP的替代定义, 并报告了关于其他分区选择机制的实用性经验观察。