  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.
