新框架支持在本地差分隐私下进行高效的分布学习

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-16 04:00

研究人员开发了一个在本地差分隐私（LDP）下学习单变量分布的新框架。该框架为包括高斯混合模型（GMM）在内的各种分布类别提供了样本高效的LDP学习器，并提供了通用的计算高效保证。该工作还包括一个在LDP下学习GMM的新下界，改进了现有界限。 AI

影响这项研究可能带来更强大、更注重隐私的机器学习模型，尤其是在数据敏感性很高的场景中。

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arXiv cs.LG TIER_1 English(EN) · Mohammad Afzali, Hassan Ashtiani, Sivan Sabato · 2026-10-16 04:00

在局部差分隐私下高效地学习单变量密度

arXiv:2610.21033v1 Announce Type: cross Abstract: We propose a general framework for non-interactive locally differentially private (LDP) learning of structured univariate distributions that can be approximated by piece-wise polynomials. This gives the first sample-efficient LDP …