English(EN) Differential Privacy of Gradient Descent on Perturbed Objectives

新分析详细介绍了梯度下降在扰动目标上的差分隐私

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-05 04:00

研究人员开发了一种方法来分析梯度下降应用于扰动目标时的差分隐私。该方法包括在优化之前向目标函数添加一个随机线性项，然后检查所得最小化器的性质。该研究提供了梯度下降迭代保持隐私的条件，特别是对于具有利普希茨海森矩阵的强凸和平滑目标。对于广义线性模型，一旦满足某些迭代条件，这种隐私分析显示出不依赖于环境维度，并且优化误差呈几何级数下降。 AI

影响为理解和潜在地改进机器学习训练过程的隐私保证提供了一个理论框架。

排序理由该集群包含一篇学术论文，详细介绍了对机器学习算法中差分隐私的新理论分析。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Austin Watkins, Raman Arora · 2026-10-05 04:00

梯度下降在扰动目标上的差分隐私

arXiv:2610.02716v1 Announce Type: new Abstract: Objective perturbation adds a random linear term to a regularized empirical risk and releases the exact perturbed minimizer. We study the finite computation obtained by releasing the $N$-th iterate of deterministic gradient descent …

本文如何被排名

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梯度下降在扰动目标上的差分隐私

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