English(EN) Faster Learning under Relaxed Local Differential Privacy

新的隐私机制加速了机器学习中的密度估计

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-07 04:00

研究人员开发了一种在宽松局部差分隐私下进行密度估计的新方法，利用对称伽马分布进行噪声添加。与传统的 $\alpha$-LDP 方法相比，该方法实现了更快的 Sobolev 平滑函数估计速率，使其更接近非私有 minimax 速率。该技术已在神经网络估计器中实现，该估计器在优化过程中避免添加额外噪声，在数值结果中显示出比 Laplace 和 private-SGD 机制显著的改进。 AI

影响这项研究可能带来更高效、更准确的隐私敏感应用机器学习模型。

排序理由详细介绍一种新的隐私保护机器学习统计方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

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报道来源 [1]

arXiv stat.ML TIER_1 English(EN) · Cristina Butucea, Huiyun Tang, Marie-Luce Taupin · 2026-09-07 04:00

在宽松局部差分隐私下实现更快的学习

arXiv:2609.05034v1 Announce Type: cross Abstract: We consider density estimation under the relaxed local differential privacy condition that the privatized distributions are $\alpha$-close in total variation distance. We show that adding independent noise with a convenient symmet…

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报道来源 [1]

在宽松局部差分隐私下实现更快的学习

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