[论文] Task-aware Kernel Stein certificates for Rényi differential privacy

论文概要

研究领域: ML 作者: Leoni Carla Wirth, Tara Trautwein, Gesine Reinert 发布时间: 2026-10-16 arXiv: 2610.23798

中文摘要

基于 Rényi 散度的 Rényi 差分隐私已成为隐私保证的首选概念。然而，在高斯设定之外，Rényi 散度可能难以计算。特别是利用任务特定信息的 Rényi 差分隐私保证（如 Rényi Pufferfish 隐私）可能更具挑战性。本文使用基于伴随分布的核 Stein 差异为 Rényi 差分隐私提供证书。通过设计核函数聚焦于相关隐私方向，我们还导出了任务感知的隐私证书。我们为高斯机制和拉普拉斯机制提供精确恒等式，为指数机制提供上界，并给出独立的任务感知保证。一个真实数据集示例说明了受保护的比较集和发布的子空间如何共同决定最终的证书。

原文摘要

Rènyi differential privacy, based on Rènyi divergence, has become a notion of choice for privacy guarantees. However, outside the Gaussian setting, Rènyi divergence can be difficult to compute. In particular, Rènyi differential privacy guarantees that exploit task-specific information, as in Rènyi Pufferfish privacy, can be challenging. Here we use kernel Stein discrepancies based on escort distributions to provide certificates for Rènyi 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 re...

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