DP-UTIL: Comprehensive Utility Analysis of Differential Privacy in Machine Learning
Ismat Jarin, Birhanu Eshete · 2022
Differential Privacy (DP) has emerged as a rigorous formalism to quantify privacy protection provided by an algorithm that operates on privacy sensitive data. In machine learning (ML), DP has been employed to limit inference/disclosure of training examples. Prior work leveraged DP across the ML pipeline, albeit in isolation, often focusing on mechanisms such as gradient perturbation.