论文标题
选择私人分区选择
Differentially private partition selection
论文作者
论文摘要
许多数据分析操作可以通过查询一组无限的分区来表达，然后进行每零件聚合。要使这样的查询有差异化的私有，在每个聚合中添加噪音是不够的：我们还需要确保发布的分区集也是私人的。 这个问题并不是什么新鲜事物，最近被正式引入了差异化集合。在这项工作中，我们继续研究该领域，并专注于每个用户与单个分区相关联的共同环境。在这种情况下，我们提出了一种简单的最佳差异私人机制，可最大程度地提高发布的分区的数量。我们讨论实施注意事项，以及这种方法可能扩展到每个用户对固定少量分区贡献的设置。
Many data analysis operations can be expressed as a GROUP BY query on an unbounded set of partitions, followed by a per-partition aggregation. To make such a query differentially private, adding noise to each aggregation is not enough: we also need to make sure that the set of partitions released is also differentially private. This problem is not new, and it was recently formally introduced as differentially private set union. In this work, we continue this area of study, and focus on the common setting where each user is associated with a single partition. In this setting, we propose a simple, optimal differentially private mechanism that maximizes the number of released partitions. We discuss implementation considerations, as well as the possible extension of this approach to the setting where each user contributes to a fixed, small number of partitions.
