High-Dimensional Asymptotics of Differentially Private PCA
The research investigates sharp privacy guarantees for Differentially Private Principal Component Analysis (PCA) in high-dimensional limits ($p o ext{infinity}$). It precisely characterizes how estimation error and privacy loss depend on the noise parameter ($eta$). The findings reveal specific phase transitions where utility degrades as noise decreases, but the fundamental privacy bounds do not improve asymptotically.