Abstract
The increasing availability of personal data has enabled significant advances in fields such as machine learning, healthcare, and cybersecurity. However, this data abundance also raises serious privacy concerns, especially in light of powerful re-identification attacks and growing legal and ethical demands for responsible data use. Differential privacy (DP) has emerged as a principled, mathematically grounded framework for mitigating these risks. This review provides a comprehensive survey of DP, covering its theoretical foundations, practical mechanisms, and real-world applications. It explores key algorithmic tools and domain-specific challenges - particularly in privacy-preserving machine learning and synthetic data generation. The report also highlights usability issues and the need for improved communication and transparency in DP systems. Overall, the goal is to support informed adoption of DP by researchers and practitioners navigating the evolving landscape of data privacy.
Abstract (translated)
个人数据的日益普及在机器学习、医疗保健和网络安全等领域取得了显著进展。然而，这种数据丰富性也引发了严重的隐私问题，尤其是在强大的重新识别攻击以及对负责任的数据使用日益增长的法律和伦理要求背景下。差分隐私（DP）作为一种原则性和数学基础坚实的框架已出现，旨在缓解这些风险。这篇综述全面介绍了差分隐私，涵盖了其理论基础、实际机制及现实世界的应用。它探讨了关键算法工具和特定领域的挑战——特别是隐私保护机器学习和合成数据生成方面的挑战。报告还强调了使用性问题以及改进差分隐私系统中沟通与透明度的需求。总体而言，目标是支持研究人员和从业者在不断变化的数据隐私领域内做出明智的差分隐私采用决策。
URL
https://arxiv.org/abs/2509.03294
