Differential Privacy of Gaussian Process Posterior Sampling

Abstract:We study the privacy of releasing posterior sample paths from a Gaussian process (GP) when the entire training set including covariates and responses is private. Unlike standard differential-privacy (DP) mechanisms that add external noise, posterior sampling is random by construction. We show that this intrinsic randomness yields DP guarantees by deriving explicit Rényi-DP bounds for GP posterior sample-path release. The bounds separate posterior-mean leakage from data-dependent posterior-covariance leakage showing that meaningful privacy depends sharply on effective ridge regularisation. We apply membership-inference attacks to show that empirical leakage follows the predicted dependence on regularisation, posterior variance and the number of released posterior sample-paths. Utility experiments on downstream posterior-sampling tasks identify noisy-observation regimes where privacy-compatible regularisation preserves useful decisions with modest utility loss. When stronger privacy is needed, the intrinsic guarantee can be sharpened by adding calibrated GP noise, providing an explicit additional privacy knob.

Machine Learning (stat.ML); Cryptography and Security (cs.CR); Machine Learning (cs.LG)

arXiv:2606.17995 [stat.ML]

(or arXiv:2606.17995v1 [stat.ML] for this version)

https://doi.org/10.48550/arXiv.2606.17995 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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