The α-Wasserstein mechanism calibrates noise for exact Rényi Pufferfish Privacy by bounding the Wasserstein metric, generalizing the W_∞ pufferfish mechanism and Rényi differential privacy results.
Noise reduction for pufferfish privacy: A practical noise calibration method
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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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$\alpha$-Wasserstein Mechanism for R\'{e}nyi Pufferfish Privacy
The α-Wasserstein mechanism calibrates noise for exact Rényi Pufferfish Privacy by bounding the Wasserstein metric, generalizing the W_∞ pufferfish mechanism and Rényi differential privacy results.
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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.