English(EN) PE-means: Improved Differentially Private $k$-means Clustering through Private Evolution

新的PE-means算法将私有k-均值聚类改进了20%

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-02 04:00

研究人员开发了PE-means，一种用于差分隐私k-均值聚类的新算法。该方法通过使用具有恒定敏感度的私有直方图，而不是直接对私有数据求和，从而改进了现有技术。与当前最先进的方法相比，PE-means的聚类损失平均减少了20%。 AI

影响引入了一种更有效的私有聚类方法，可能提高机器学习应用中的数据隐私性。

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arXiv cs.LG TIER_1 English(EN) · Thomas Humphries, Zinan Lin, Sergey Yekhanin · 2026-06-02 04:00

PE-means：通过私有演化改进差分隐私k-means聚类

arXiv:2606.00342v1 Announce Type: new Abstract: We study the problem of differentially private (DP) $k$-means clustering in Euclidean space. Previous solutions rely on summing the private data directly, which induces a sensitivity proportional to the domain. We introduce PE-means…