Propose, Test, Release: Differentially private estimation with high probability
This provides foundational improvements in privacy-preserving statistics for data analysts, though it builds incrementally on existing PTR mechanisms.
The paper tackles the problem of differentially private estimation of median and mean without boundedness assumptions on data or known intervals for parameters, achieving the first sub-Gaussian high probability bounds for possibly heavy-tailed variables.
We derive concentration inequalities for differentially private median and mean estimators building on the "Propose, Test, Release" (PTR) mechanism introduced by Dwork and Lei (2009). We introduce a new general version of the PTR mechanism that allows us to derive high probability error bounds for differentially private estimators. Our algorithms provide the first statistical guarantees for differentially private estimation of the median and mean without any boundedness assumptions on the data, and without assuming that the target population parameter lies in some known bounded interval. Our procedures do not rely on any truncation of the data and provide the first sub-Gaussian high probability bounds for differentially private median and mean estimation, for possibly heavy tailed random variables.