Information about us, our actions, and our preferences is created at scale through surveys or scientific studies or as a result of our interaction with digital devices such as smartphones and fitness trackers. The ability to safely share and analyze such data is key for scientific and societal progress. Anonymization is considered by scientists and policy-makers as one of the main ways to share data while minimizing privacy risks. In this review, we offer a pragmatic perspective on the modern literature on privacy attacks and anonymization techniques. We discuss traditional de-identification techniques and their strong limitations in the age of big data. We then turn our attention to modern approaches to share anonymous aggregate data, such as data query systems, synthetic data, and differential privacy. We find that, although no perfect solution exists, applying modern techniques while auditing their guarantees against attacks is the best approach to safely use and share data today.

中文翻译：

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匿名化：在保护隐私的同时使用数据的不完美科学


有关我们、我们的行为和偏好的信息是通过调查或科学研究大规模创建的，或者是我们与智能手机和健身追踪器等数字设备互动的结果。安全共享和分析此类数据的能力是科学和社会进步的关键。科学家和政策制定者认为匿名是共享数据同时最大限度降低隐私风险的主要方式之一。在这篇评论中，我们对有关隐私攻击和匿名化技术的现代文献提供了务实的视角。我们讨论传统的去识别技术及其在大数据时代的强大局限性。然后，我们将注意力转向共享匿名聚合数据的现代方法，例如数据查询系统、合成数据和差异隐私。我们发现，尽管不存在完美的解决方案，但应用现代技术并同时审核其针对攻击的保证是当今安全使用和共享数据的最佳方法。