3 papers
cs.LG2026
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…
cs.LG2024
Private Regression via Data-Dependent Sufficient Statistic Perturbation
Cecilia Ferrando, Daniel Sheldon
Sufficient statistic perturbation (SSP) is a widely used method for differentially private linear regression. SSP adopts a data-independent approach where privacy noise from a simp…
cs.LG2021
Combining Public and Private Data
Cecilia Ferrando, Jennifer Gillenwater, Alex Kulesza
Differential privacy is widely adopted to provide provable privacy guarantees in data analysis. We consider the problem of combining public and private data (and, more generally, d…