English(EN) Efficient and Optimal Learning of Discrete Distributions with Person-Level Privacy

新算法在离散分布上实现了最优的个体级别差分隐私

作者 PulseAugur 编辑部 · [2 个来源] · 2026-10-22 17:40

研究人员开发了新的算法，用于在批处理设置中学习离散分布，同时遵守个体级别的差分隐私。这些算法在纯差分隐私和近似差分隐私下都实现了近乎最优的保证，改进了先前的方法。一项关键创新是利用半定规划和Grothendieck不等式来确保隐私的新型6次多项式平方和估计器。 AI

影响推进了隐私保护机器学习的理论理解和实践方法，可能支持更敏感的数据分析。

排序理由该集群包含一篇学术论文，讨论了针对特定机器学习问题的 novel 算法和理论界限。

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

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arXiv cs.LG TIER_1 English(EN) · Gautam Kamath, Mahbod Majid, Ankur Moitra, Argyris Mouzakis, Jonathan Ullman · 2026-10-23 04:00

Efficient and Optimal Learning of Discrete Distributions with Person-Level Privacy

arXiv:2610.31612v1 Announce Type: new Abstract: We study learning a discrete distribution under person-level differential privacy in the batched setting. Given $n$ users, each contributing $m$ i.i.d. samples from an unknown distribution $\mu$ over $[k]$, we seek an estimate of $\…
Hugging Face Daily Papers TIER_1 English(EN) · 2026-10-22 17:40

Efficient and Optimal Learning of Discrete Distributions with Person-Level Privacy

We study learning a discrete distribution under person-level differential privacy in the batched setting. Given $n$ users, each contributing $m$ i.i.d. samples from an unknown distribution $μ$ over $[k]$, we seek an estimate of $μ$ with total-variation error $α$. We give polynomi…