Auditing $f$-Differential Privacy in One Run

ICLR 2025 Conference Submission12235 Authors

Keywords: Differential privacy, Auditing privacy

TL;DR: We use trade-off functions to perform tighter auditing of algorithms designed to satisfy differential privacy in a single run.

Abstract: Empirical auditing has emerged as a means of catching some of the flaws in the implementation of privacy-preserving algorithms. Existing auditing mechanisms, however, are either computationally inefficient -- requiring multiple runs of the machine learning algorithms —- or suboptimal in calculating an empirical privacy. In this work, we present a tight and efficient auditing procedure and analysis that can effectively assess the privacy of mechanisms. Our approach is efficient; similar to the recent work of Steinke, Nasr, and Jagielski (2023), our auditing procedure leverages the randomness of examples in the input dataset and requires only a single (training) run of the target mechanism. And it is more accurate; we provide a novel analysis that enables us to achieve tight empirical privacy estimates by using the hypothesized $f$-DP curve of the mechanism, which provides a more accurate measure of privacy than the traditional $\epsilon,\delta$ differential privacy parameters. We use our auditing procure and analysis to obtain empirical privacy, demonstrating that our auditing procedure delivers tighter privacy estimates.

Primary Area: alignment, fairness, safety, privacy, and societal considerations

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Submission Number: 12235