Differential Privacy in Gaussian Process Posterior Sampling

Gaussian process posterior sampling inherently provides differential privacy due to its intrinsic randomness. Explicit Rényi-DP bounds show that privacy depends on ridge regularisation, with membership-inference attacks confirming the predicted leakage patterns. Adding calibrated GP noise enhances privacy while maintaining utility in downstream tasks.