On Choosing the $μ$ Parameter in Gaussian Differential Privacy
Bogdan Kulynych, Antti Honkela
cs.LG stat.ML
Abstract
Recent work argues for using Gaussian differential privacy (GDP) to report the privacy guarantees in privacy-preserving machine learning. We provide principled mappings from pure-DP $\varepsilon$ to GDP $μ$ by matching the worst-case success of a strong-adversary membership inference attack in terms of three metrics: multiplicative advantage at fixed FPR, precision at fixed recall, and the standard privacy profile. We tabulate $μ$ values across a useful range of parameters and recommend $μ\approx \varepsilon/5$ as a conservative general-purpose conversion.
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Classified with taxonomy v2 on Wed, 2 Sept 2026.