English(EN) Optimal Guarantees for Auditing R\'enyi Differentially Private Machine Learning

新框架为审计RDP机器学习提供最优保证

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-22 04:00

研究人员开发了一个新的审计框架，用于审计声称具有Rényi差分隐私（RDP）的机器学习算法。该框架使用Donsker-Varadhan（DV）估计器直接测量Rényi散度，为RDP审计提供明确的置信区间。所提出的方法实现了信息论最优的样本复杂度保证，并在经验上优于现有的黑盒方法，尤其是在具有挑战性的小和中等Rényi阶数方面。 AI

影响为审计机器学习模型的隐私建立了新的最优保证，可能提高已部署系统的信任度和安全性。

排序理由该集群包含一篇学术论文，详细介绍了审计差分隐私机器学习算法的新理论框架和经验验证。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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Signal score: 0 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该集群包含一篇学术论文，详细介绍了审计差分隐私机器学习算法的新理论框架和经验验证。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Benjamin D. Kim, Lav R. Varshney, Daniel Alabi · 2026-05-22 04:00

R\'enyi差分隐私机器学习审计的最优保证

arXiv:2605.21938v1 Announce Type: new Abstract: We study black-box auditing for machine learning algorithms that claim R \ 'enyi differential privacy (RDP) guarantees. We introduce an auditing framework, based on hypothesis testing, that directly estimates R\'enyi divergence betw…

本文如何被排名

报道来源 [1]

R\'enyi差分隐私机器学习审计的最优保证

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