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.

差分隐私已成为机器学习中广受欢迎的数据保护方法，尤其是因为它允许提供严格的数学隐私保证。本文综述了差分隐私集中深度学习的现状，对最近的进展和开放问题进行了深入分析，并讨论了该领域未来的可能发展。通过系统的文献综述，涵盖了以下主题：针对隐私模型的审计和评估方法，提高隐私与效用之间的权衡，防范各种威胁和攻击，差分隐私生成模型以及新兴应用领域。