PACE-GGM selects poorly approximated covariance entries, measures them privately, and reconstructs the full matrix with a maximum-entropy objective to produce a Gaussian graphical model, yielding lower estimation error than uniform perturbation.
Privsyn: Differentially private data synthesis
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Private Adaptive Covariance Estimation via Gaussian Graphical Models
PACE-GGM selects poorly approximated covariance entries, measures them privately, and reconstructs the full matrix with a maximum-entropy objective to produce a Gaussian graphical model, yielding lower estimation error than uniform perturbation.