On Inherent Privacy of Posterior Sampling: A Unified R\'enyi-Divergence Framework

Differential Privacy (DP) provides a rigorous framework for quantifying privacy guarantees. While most methods achieve DP by injecting calibrated random noise, the intrinsic randomness of certain procedures such as sampling can yield DP guarantees ``for free''.