English(EN) Gaussian Approximation for Composition and Subsampling of Differentially Private Algorithms

新的高斯近似增强了差分隐私算法分析

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-28 04:00

研究人员开发了一种分析差分隐私算法隐私保证的新方法，特别关注差分隐私随机梯度下降（DP-SGD）。新方法使用高斯近似来约束子采样机制组合的隐私损失，通过考虑独立随机变量之和的近似高斯性质，提供了比以前方法更紧密的界限。该技术提供了更准确的隐私分析，特别是对于高斯机制和像球箱采样这样的随机分配方法，从而提高了隐私保护机器学习的准确性。 AI

影响提高了机器学习模型的隐私保证，可能允许使用更敏感的数据进行训练。

排序理由该集群包含一篇详细介绍差分隐私分析新理论方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Vitaly Feldman, Moshe Shenfeld · 2026-10-28 04:00

差分隐私算法的组合与子采样的高斯近似

arXiv:2610.33333v1 Announce Type: new Abstract: The privacy analysis of differentially private stochastic gradient descent (DP-SGD) relies on bounds for the composition of subsampled mechanisms. The privacy of such a composition is bounded by that of a pair of product distributio…

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差分隐私算法的组合与子采样的高斯近似

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