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.

翻译：近年来，关于差分隐私（DP）及其各类应用的研究呈爆炸式增长，涵盖从差分隐私的新型变体与核算技术、蓬勃发展的差分隐私机器学习（DPML）领域，到各公司及组织（如人口普查局）等实际场景中的新近实现方法。现有综述多聚焦于差分隐私在特定场景（如数据发布、特定机器学习任务、非结构化数据分析、位置隐私等）中的应用。本文旨在填补这一空白，系统综述差分隐私理论的最新进展，包括雷尼差分隐私（Renyi DP）与集中差分隐私（Concentrated DP）等新型变体、创新机制与技术，以及差分隐私机器学习领域的理论发展。此外，本文还探讨了差分隐私在保护隐私的机器学习实践中的应用，并介绍了几种实际部署方案。