Advances in Differential Privacy and Differentially Private Machine Learning

Abstract

There has been an explosion of research on differential privacy (DP) and its various applications in recent years, ranging from novel variants and accounting techniques in differential privacy to the thriving field of differentially private machine learning (DPML) to newer implementations in practice, like those by various companies and organisations such as census bureaus. Most recent surveys focus on the applications of differential privacy in particular contexts like data publishing, specific machine learning tasks, analysis of unstructured data, location privacy, etc. This work thus seeks to fill the gap for a survey that primarily discusses recent developments in the theory of differential privacy along with newer DP variants, viz. Renyi DP and Concentrated DP, novel mechanisms and techniques, and the theoretical developments in differentially private machine learning in proper detail. In addition, this survey discusses its applications to privacy-preserving machine learning in practice and a few practical implementations of DP.

References in corpus (9)

• Deep Learning with Differential Privacy
• RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response
• Renyi Differential Privacy
• Scalable Private Learning with PATE
• A Comprehensive Survey on Local Differential Privacy Toward Data Statistics and Analysis
• ARA : Aggregated RAPPOR and Analysis for Centralized Differential Privacy
• The Sparse Vector Technique, Revisited
• DPlis: Boosting Utility of Differentially Private Deep Learning via Randomized Smoothing
• Locally Private Graph Neural Networks