Hypothesis testing interpretations and Renyi differential privacy
Files
First author draft
Date
2019
DOI
Authors
Balle, Borja
Barthe, Gilles
Gaboardi, Marco
Hsu, Justin
Sato, Tetsuya
Version
OA Version
Citation
B. Balle, G. Barthe, M. Gaboardi, J. Hsu, T. Sato. "Hypothesis Testing Interpretations and Renyi Differential Privacy." Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, PMLR 108:2496-2506, 2020.. International Conference on Artificial Intelligence and Statistics https://arxiv.org/abs/1905.09982
Abstract
Differential privacy is a de facto standard in data privacy, with applications in the public and private sectors. A way to explain differential privacy, which is particularly appealing to statistician and social scientists, is by means of its statistical hypothesis testing interpretation. Informally, one cannot effectively test whether a specific individual has contributed her data by observing the output of a private mechanism—any test cannot have both high significance and high power.
In this paper, we identify some conditions under which a privacy definition given in terms of a statistical divergence satisfies a similar interpretation. These conditions are useful to analyze the distinguishability power of divergences and we use them to study the hypothesis testing interpretation of some relaxations of
differential privacy based on Rényi divergence. This analysis also results in an improved conversion rule between these definitions and differential privacy.