Adaptive Privacy Composition for Accuracy-first Mechanisms

Ryan Rogers, Gennady Samorodnitsky, Steven Wu, Aaditya Ramdas

Keywords: differential privacy, brownian motion, composition, martingale

TL;DR: We develop privacy filters that allow an analyst to adaptively switch between differentially private mechanisms and ex-post private mechanisms with an accuracy constraint subject to an overall privacy loss guarantee.

Abstract:

Although there has been work to develop ex-post private mechanisms from Ligett et al. '17 and Whitehouse et al '22 that seeks to provide privacy guarantees subject to a target level of accuracy, there was not a way to use them in conjunction with differentially private mechanisms. Furthermore, there has yet to be work in developing a theory for how these ex-post privacy mechanisms compose, so that we can track the accumulated privacy over several mechanisms. We develop privacy filters that allow an analyst to adaptively switch between differentially private mechanisms and ex-post private mechanisms subject to an overall privacy loss guarantee.
We show that using a particular ex-post private mechanism --- noise reduction mechanisms --- can substantially outperform baseline approaches that use existing privacy loss composition bounds. We use the common task of returning as many counts as possible subject to a relative error guarantee and an overall privacy budget as a motivating example.

Submission Number: 9102