English(EN) Gap-free Differentially Private PCA for Gaussian Data

arXiv发布了适用于高斯数据的新差分隐私PCA算法

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-29 04:00

研究人员开发了一种新的差分隐私主成分分析（PCA）算法，该算法专门针对高斯数据设计。该算法旨在提供无间隙的隐私保证，确保敏感信息得到保护，同时仍能进行有效的数据分析。这项工作已在arXiv上发表，并与各种代码和引用管理工具及平台相关联。 AI

排序理由该集群包含一篇在arXiv上发表的详细介绍新算法的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Alina Ene, Huy L. Nguyen · 2026-09-29 04:00

Gap-free Differentially Private PCA for Gaussian Data

arXiv:2609.31614v1 Announce Type: cross Abstract: We give a gap-free differentially private algorithm for the principal component analysis (PCA) problem with Gaussian data.