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Membership Inference Privacy-Preserving Algorithm Privacy-Preserving Machine Learning
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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 ε 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 ≈ ε/5 as a conservative general-purpose conversion.