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.

翻译：差分隐私已成为机器学习中广泛使用的数据保护方法，尤其是其能制定严格的数学隐私保障。本综述系统梳理了差分隐私集中式深度学习的当前研究前沿，深入分析了最新进展与开放性问题，并探讨了该领域潜在的未来发展。基于系统性文献综述，本文重点关注以下主题：隐私模型的审计与评估方法、隐私-效用权衡的改进、针对广泛威胁与攻击的防护、差分隐私生成模型，以及新兴应用领域。