English(EN) Private Learning with Public Feature Conditioning

新的Cond-DP方法改进了差分隐私回归

作者 PulseAugur 编辑部 · [2 个来源] · 2026-06-17 07:34

研究人员开发了Cond-DP，一种新颖的差分隐私回归方法，该方法利用公共的、非敏感的特征来增强优化。该方法是DPSGD的条件变体，它采用数据驱动的条件矩阵来重塑优化景观并加速收敛。Cond-DP在各种数据集和模型架构上始终优于现有基线，为私有回归任务提供了更有效的解决方案。 AI

影响这项研究为训练具有敏感数据的模型提供了一种更有效的方法，有可能改进推荐系统等领域的隐私保护AI应用。

排序理由该集群包含一篇详细介绍差分隐私回归新方法的论文。

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.AI TIER_1 English(EN) · Shuli Jiang, Walid Krichene, Nicolas Mayoraz · 2026-06-18 04:00

公共特征条件下的私有学习

arXiv:2606.18773v1 Announce Type: cross Abstract: We study differentially private (DP) regression in settings where each data sample includes public, non-sensitive features -- common in applications such as recommendation and advertising systems. While such label-DP or semi-sensi…
arXiv cs.AI TIER_1 English(EN) · Nicolas Mayoraz · 2026-06-17 07:34

带公共特征条件的私有学习

We study differentially private (DP) regression in settings where each data sample includes public, non-sensitive features -- common in applications such as recommendation and advertising systems. While such label-DP or semi-sensitive-feature settings have been primarily explored…