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This paper presents a theoretical advancement in differential privacy by eliminating performance gaps for PCA on Gaussian datasets, directly addressing core challenges in machine learning security and statistical inference. The work contributes to the growing field of private machine learning, offering improved utility guarantees without sacrificing privacy protections.
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Gap-free Differentially Private PCA for Gaussian Data
arXiv1 min de cititArticolul și perspectiva sunt în engleză.
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.
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