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arXiv · 2606.09582

On Choosing the $\mu$ Parameter in Gaussian Differential Privacy

Bogdan Kulynych·Antti Honkela
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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 $\mu$ 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 $\mu$ values across a useful range of parameters and recommend $\mu \approx \varepsilon/5$ as a conservative general-purpose conversion.

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Bogdan Kulynych, Antti Honkela. 2026-06-08. On Choosing the $\mu$ Parameter in Gaussian Differential Privacy. https://arxiv.org/abs/2606.09582

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