Statistical Quantification of Differential Privacy: A Local Approach

Önder Askin; Tim Kutta; Holger Dette

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arXiv · 2108.09528

Statistical Quantification of Differential Privacy: A Local Approach

Önder Askin·Tim Kutta·Holger Dette

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Abstract

In this work, we introduce a new approach for statistical quantification of differential privacy in a black box setting. We present estimators and confidence intervals for the optimal privacy parameter of a randomized algorithm $A$, as well as other key variables (such as the "data-centric privacy level"). Our estimators are based on a local characterization of privacy and in contrast to the related literature avoid the process of "event selection" - a major obstacle to privacy validation. This makes our methods easy to implement and user-friendly. We show fast convergence rates of the estimators and asymptotic validity of the confidence intervals. An experimental study of various algorithms confirms the efficacy of our approach.

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Önder Askin, Tim Kutta, Holger Dette. 2022-05-02. Statistical Quantification of Differential Privacy: A Local Approach. https://arxiv.org/abs/2108.09528

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