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.

翻译：分区选择（或称集合合并）是差分隐私机制设计中的一个重要原语：在每位用户贡献一个项目列表的数据库中，目标是在满足差分隐私的前提下尽可能多地发布这些项目。本文提出了一种用于差分隐私分区选择的新型机制。该机制——我们称之为 DP-SIPS——非常简洁：它通过多次迭代对数据集应用朴素算法，在每一步中从数据集中移除已发布的分区，同时增加隐私预算。这种方法保留了朴素机制的可扩展性优势，但其效用可与先前工作中更复杂的方法相媲美。