Sharper information-theoretic generalization bounds for differentially private algorithms obtained via typicality arguments that improve prior mutual-information results and add new maximal-leakage bounds.
R ´enyi differential privacy,
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Gaussian mechanisms for Rényi Pufferfish Privacy under Gaussian and mixture priors deliver exact divergence derivations, closed-form sufficient conditions, and 48.9% less noise than additive baselines on statistical and model queries.
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On the Generalization Error of Differentially Private Algorithms via Typicality
Sharper information-theoretic generalization bounds for differentially private algorithms obtained via typicality arguments that improve prior mutual-information results and add new maximal-leakage bounds.
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R\'enyi Pufferfish Privacy with Gaussian-based Priors: From Single Gaussian to Mixture Model
Gaussian mechanisms for Rényi Pufferfish Privacy under Gaussian and mixture priors deliver exact divergence derivations, closed-form sufficient conditions, and 48.9% less noise than additive baselines on statistical and model queries.