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Private Adaptive Covariance Estimation via Gaussian Graphical Models
Cecilia Ferrando, Miguel Fuentes, Brett Mullins +2
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 c…
Efficient and Private Marginal Reconstruction with Local Non-Negativity
Brett Mullins, Miguel Fuentes, Yingtai Xiao +3
Differential privacy is the dominant standard for formal and quantifiable privacy and has been used in major deployments that impact millions of people. Many differentially private…
Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Data
Miguel Fuentes, Brett Mullins, Ryan McKenna +2
Mechanisms for generating differentially private synthetic data based on marginals and graphical models have been successful in a wide range of settings. However, one limitation of…