Differential Privacy of Gaussian Process Posterior Sampling

This paper studies the privacy guarantees of releasing posterior sample paths from a Gaussian process when the entire training set, including covariates and responses, is considered private. Rather than relying on standard differential-privacy mechanisms that add external noise, the analysis focuses on the randomness inherent in posterior sampling itself. It provides a formal treatment of how much privacy such released sample paths preserve.