论文标题
动态数据的差异隐私
Differential Privacy on Dynamic Data
论文作者
论文摘要
差异隐私的一个基本问题是在数据集上发布私有化的数据结构，该数据结构可用于回答具有小错误的线性查询。在静态案例中，对此问题进行了很好的研究。在本文中，我们考虑了可以随时间插入或从数据集中插入或从数据集中删除的动态设置，并且我们需要不断发布数据结构，以便可以随时回答查询。我们介绍了这种动态差异机制的黑盒构造，从静态机制中仅具有静态降解的静态机制。对于完全动态的情况，这就是第一个结果。对于仅插入的情况，已知类似的结构，但我们通过稀疏更新流进行了改进。
A fundamental problem in differential privacy is to release a privatized data structure over a dataset that can be used to answer a class of linear queries with small errors. This problem has been well studied in the static case. In this paper, we consider the dynamic setting where items may be inserted into or deleted from the dataset over time, and we need to continually release data structures so that queries can be answered at any time. We present black-box constructions of such dynamic differentially private mechanisms from static ones with only a polylogarithmic degradation in the utility. For the fully-dynamic case, this is the first such result. For the insertion-only case, similar constructions are known, but we improve them over sparse update streams.
