Mining health data can lead to faster medical decisions, improvement in the quality of treatment, disease prevention, and reduced cost, and it drives innovative solutions within the healthcare sector. However, health data are highly sensitive and subject to regulations such as the General Data Protection Regulation, which aims to ensure patient's privacy. Anonymization or removal of patient identifiable information, although the most conventional way, is the first important step to adhere to the regulations and incorporate privacy concerns. In this article, we review the existing anonymization techniques and their applicability to various types (relational and graph based) of health data. Besides, we provide an overview of possible attacks on anonymized data. We illustrate via a reconstruction attack that anonymization, although necessary, is not sufficient to address patient privacy and discuss methods for protecting against such attacks. Finally, we discuss tools that can be used to achieve anonymization.

中文翻译：

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医疗保健数据匿名化回顾


挖掘健康数据可以加快医疗决策、提高治疗质量、预防疾病并降低成本，并推动医疗保健行业的创新解决方案。然而，健康数据高度敏感，并受到《通用数据保护条例》等法规的约束，该条例旨在确保患者的隐私。匿名化或删除患者可识别信息虽然是最传统的方式，但却是遵守法规并纳入隐私问题的第一个重要步骤。在本文中，我们回顾了现有的匿名技术及其对各种类型（基于关系和基于图表）的健康数据的适用性。此外，我们还概述了对匿名数据可能发生的攻击。我们通过重建攻击说明，匿名化虽然是必要的，但不足以解决患者隐私问题，并讨论了防止此类攻击的方法。最后，我们讨论可用于实现匿名化的工具。