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.

翻译：差分隐私中的一个基本问题是在数据集上发布一个私有化数据结构，该结构可用于以较小误差回答一类线性查询。该问题在静态情形下已得到充分研究。本文考虑动态场景：数据项会随时间推移被插入或删除，因此需要持续发布数据结构，以便在任何时刻都能回答查询。我们提出了从静态机制到动态差分隐私机制的黑盒构建方法，仅带来多对数级别的效用衰减。对于全动态情形，这是首个此类结果。对于仅插入情形，已知存在类似构建方法，但我们在稀疏更新流上对其进行了改进。