Differential Privacy of Gaussian Process Posterior Sampling
We study the privacy of releasing functional 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 inject external noise, posterior sampling is intrinsically random and we show that this randomness provides useful privacy guarantees. We derive Rényi-DP guarantees separating privacy leakage through the posterior mean from a distinct channel induced by the data-dependent posterior covariance. The analysis identifies effective ridge regularisation and covariance scale as the principal privacy-controlling quantities and yields sharper guarantees in several regime...
| Authors | Tomasz Maciazek |
|---|---|
| Primary category | stat.ML |
| Published | 16 June 2026 |
| Last updated at source | 14 September 2026 |
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