arXiv:2610.23798v1 Announce Type: new
Abstract: R\enyi differential privacy, based on R\enyi divergence, has become a notion of choice for privacy guarantees. However, outside the Gaussian setting, R\enyi divergence can be difficult to compute. In particular, R\enyi differential privacy guarantees that exploit task-specific information, as in R\enyi Pufferfish privacy, can be challenging. Here we use kernel Stein discrepancies based on escort distributions to provide certificates for R\enyi differential privacy. By designing the kernel to focus on the relevant privacy directions, we also derive task-aware privacy certificates. We provide exact identities for Gaussian and Laplace mechanisms and bounds for exponential mechanisms, with separate task-aware guarantees. A real data set example illustrates how the protected comparison set and the released subspace jointly determine the resulting certificates.