Parametric Bootstrap for Differentially Private Confidence Intervals

Understand

The goal of this paper is to develop a practical and general-purpose approach to construct confidence intervals for differentially private parametric estimation.

• We find that the parametric bootstrap is a simple and effective solution.
• It cleanly reasons about variability of both the data sample and the randomized privacy mechanism and applies "out of the box" to a wide class of private estimation routines.
• It can also help correct bias caused by clipping data to limit sensitivity.