  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: auditing and evaluation methods for private
models, improvements of privacy-utility trade-offs, protection against a broad
range of threats and attacks, differentially private generative models, and
emerging application domains.
