Fast Estimation for Privacy and Utility in Differentially Private Machine Learning

no code implementations • 1 Jan 2021 • Yuzhe Li, Yong liu, Weipinng Wang, Bo Li, Nan Liu

In this paper, we deduce the influence of $\epsilon$ on utility private learning models through strict mathematical derivation, and propose a novel approximate approach for estimating the utility of any $\epsilon$ value.

BIG-bench Machine Learning