A General Framework for Accurate and Private Mean Estimation

Zhouhao Yang, Xingyu Xu, Yuantao Gu

Abstract: In this letter, we present a differentially private algorithm which accurately estimates the mean of an underlying population with given cumulative distribution function. Our algorithm outperforms the former algorithms in two aspects. First, our algorithm is capably of handling more general types of probability distributions, possibly with a very heavy tail. Second, for light-tailed distributions, our algorithm achieves a better level of accuracy with fewer samples.