Universal Private Estimators

Abstract

We present \textit{universal} estimators for the statistical mean, variance, and scale (in particular, the interquartile range) under pure differential privacy. These estimators are universal in the sense that they work on an arbitrary, unknown continuous distribution $\mathcal{P}$ over $\mathbb{R}$, while yielding strong utility guarantees except for ill-behaved $\mathcal{P}$. For certain distribution families like Gaussians or heavy-tailed distributions, we show that our universal estimators match or improve existing estimators, which are often specifically designed for the given family and under \textit{a priori} boundedness assumptions on the mean and variance of $\mathcal{P}$. This is the first time these boundedness assumptions are removed under pure differential privacy. The main technical tools in our development are instance-optimal empirical estimators for the mean and quantiles over the unbounded integer domain, which can be of independent interest.

References in corpus (5)

• Differentially Private Learning with Adaptive Clipping
• AdaCliP: Adaptive Clipping for Private SGD
• The Cost of Privacy: Rates of Convergence for Parameter Estimation with Differential Privacy
• Private Mean Estimation of Heavy-Tailed Distributions
• Lower Bounds for Locally Private Estimation via Communication Complexity