English(EN) On the Limits of Sparse Linear Model with Shuffled Differential Privacy

新研究探讨稀疏线性模型中的隐私局限性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-27 04:00

本文研究了在采用置乱差分隐私（SDP）时稀疏线性回归模型的局限性。研究人员为非交互式SDP协议设定了一个下界，表明存在固有的维度障碍。他们还提出了一种接近该下界的非交互式算法。对于交互式用户级别设置，开发了一种两轮算法，该算法可以恢复支持集并以更高的准确性估计参数，显示出在用户级别差分隐私的新下界面前接近最优。 AI

影响这项研究有助于理解统计建模中隐私和准确性之间的权衡，这对于开发更强大、更安全的机器学习系统具有重要意义。

排序理由该集群包含一篇关于差分隐私在机器学习中的理论局限性和算法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

Yan Zhang

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

Signal score: 8 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该集群包含一篇关于差分隐私在机器学习中的理论局限性和算法的学术论文。[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) · Yan Zhang, Jiong Tuo, Meng Ding, Yuheng Ma, Di Wang · 2026-10-27 04:00

关于带打乱差分隐私的稀疏线性模型的局限性

arXiv:2610.32822v2 Announce Type: new Abstract: Sparse linear regression is a fundamental problem in high-dimensional statistics, and its private counterparts leave a large gap between the central and the local model of differential privacy. Shuffled differential privacy (SDP) si…

本文如何被排名

报道来源 [1]

关于带打乱差分隐私的稀疏线性模型的局限性

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