Paper Overview
Field: Machine Learning Authors: Leoni Carla Wirth, Tara Trautwein, Gesine Reinert Published: 2026-10-16 arXiv: 2610.23798
Summary
Renyi differential privacy, based on Renyi divergence, has become a notion of choice for privacy guarantees. However, outside the Gaussian setting, Renyi divergence can be difficult to compute. In particular, Renyi differential privacy guarantees that exploit task-specific information, as in Renyi Pufferfish privacy, can be challenging.
This paper uses kernel Stein discrepancies based on escort distributions to provide certificates for Renyi differential privacy. By designing the kernel to focus on the relevant privacy directions, the authors also derive task-aware privacy certificates.
Contributions
- Kernel Stein discrepancy certificates for Renyi differential privacy using escort distributions.
- Task-aware privacy certificates via kernels focused on relevant privacy directions.
- Exact identities for the Gaussian and Laplace mechanisms.
- Upper bounds for exponential mechanisms, with separate task-aware guarantees.
- A real dataset example illustrating how the protected comparison set and the released subspace jointly determine the final certificate.
*Automatically collected on 2026-10-20*