Recent Advances of Differential Privacy in Centralized Deep Learning: A Systematic Survey

Abstract

Differential Privacy has become a widely popular method for data protection in machine learning, especially since it allows formulating strict mathematical privacy guarantees. This survey provides an overview of the state-of-the-art of differentially private centralized deep learning, thorough analyses of recent advances and open problems, as well as a discussion of potential future developments in the field. Based on a systematic literature review, the following topics are addressed: emerging application domains, differentially private generative models, auditing and evaluation methods for private models, protection against a broad range of threats and attacks, and improvements of privacy-utility trade-offs.