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.

翻译：日益普及的个人数据获取推动了机器学习、医疗健康与网络安全等领域的重大进展。然而，数据丰裕也引发了严重的隐私担忧，尤其面对强大的重识别攻击以及日益增长的负责任数据使用的法律与伦理诉求。差分隐私（DP）已成为缓解这些风险的一种基于数学原理的原则性框架。本综述对DP进行了全面调研，涵盖其理论基础、实用机制及现实应用。文章探讨了关键算法工具及特定领域的挑战——尤其在隐私保护机器学习与合成数据生成方面。报告还强调了可用性问题，并指出在DP系统中改善沟通与透明度的必要性。总体而言，本文旨在支持研究人员与实践者在不断发展的数据隐私格局中，明智地采纳DP技术。