Differential Privacy on Dynamic Data

TL;DR

This paper introduces methods for maintaining differentially private data structures in dynamic datasets, allowing continual query answering with minimal utility loss, including the first solutions for fully dynamic data.

Contribution

It provides black-box constructions that extend static differential privacy mechanisms to dynamic settings with polylogarithmic utility degradation, including the first for fully dynamic data.

Findings

First fully dynamic differentially private mechanisms.

Polylogarithmic utility degradation in dynamic settings.

Improved methods for insertion-only streams.

Abstract

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.