AI RESEARCH
Private Adaptive Covariance Estimation via Gaussian Graphical Models
arXiv CS.LG
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ArXi:2605.24295v1 Announce Type: new We propose PACE-GGM, a data-adaptive differentially private method for covariance estimation that concentrates its privacy budget on the most informative entries of the empirical covariance matrix, rather than perturbing all entries. This applies in the natural setting where the modeler supplies separate bounds for each variable, so that individual entries can be measured with less noise than the full matrix.