详解差分隐私理论与应用，助你安全使用个人数据。
A Comprehensive Guide to Differential Privacy: From Theory to User Expectations
- 从数学原理到实用技术，系统梳理差分隐私方法。
- 揭示隐私保护机器学习与合成数据中的关键挑战。
- 适合关注数据安全的研究者与从业者阅读。
个人数据的广泛可用性推动了机器学习、医疗健康和网络安全等领域的显著进展，但也引发了严重的隐私担忧，尤其在强大的重识别攻击和日益增长的法律伦理要求背景下。差分隐私（DP）作为一种有理论基础的数学框架，被提出以缓解这些风险。本文全面综述差分隐私，涵盖其理论基础、实际机制及真实世界应用，探讨隐私保护机器学习与合成数据生成中的关键算法工具与领域特定挑战。报告还强调了系统可用性问题，以及提升差分隐私系统中沟通与透明度的必要性。总体目标是帮助研究人员和实践者在不断演变的数据隐私环境中做出明智决策。
原文摘要 · Abstract (English)
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.
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