Differential Privacy: What is all the noise about?
It serves as an educational resource for ML practitioners struggling to understand DP, but is incremental as it reviews existing knowledge.
This paper provides an overview of Differential Privacy (DP) concepts and applications in Machine Learning, focusing on its intersection with Federated Learning, without presenting new experimental results or numbers.
Differential Privacy (DP) is a formal definition of privacy that provides rigorous guarantees against risks of privacy breaches during data processing. It makes no assumptions about the knowledge or computational power of adversaries, and provides an interpretable, quantifiable and composable formalism. DP has been actively researched during the last 15 years, but it is still hard to master for many Machine Learning (ML)) practitioners. This paper aims to provide an overview of the most important ideas, concepts and uses of DP in ML, with special focus on its intersection with Federated Learning (FL).