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Abstract：
The digital health space is growing rapidly, and so is the interest in sharing anonymized health data. However, data anonymization techniques have yet to see much coverage in the medical literature. The purpose of this article is, therefore, to provide a practical framework for anonymization with a focus on the unique properties of data from digital health applications. Literature trends, as well as common anonymization techniques, were synthesized into a framework that considers the opportunities and challenges of digital health data. A rationale for each design decision is provided, and the advantages and disadvantages are discussed. We propose a framework based on storing data separately, anonymizing the data where the identified data is located, only exporting selected data, minimizing static attributes, ensuring k-anonymity of users and their static attributes, and preventing defined metrics from acting as quasi-identifiers by using aggregation, rounding, and capping. Data anonymization requires a pragmatic approach that preserves the utility of the data while minimizing reidentification risk. The proposed framework should be modified according to the characteristics of the respective data set.
摘要：
数字健康空间正在快速增长，共享匿名健康数据的兴趣也是如此。然而,数据匿名化技术在医学文献中还没有看到很多报道。本文的目的是,因此,提供一个实用的匿名化框架，重点关注数字健康应用程序数据的独特属性。文学趋势,以及常见的匿名化技术，被合成为一个考虑数字健康数据的机遇和挑战的框架。提供了每个设计决策的基本原理，并对其优缺点进行了讨论。我们提出了一个基于单独存储数据的框架，将识别的数据所在的数据匿名化，仅导出选定的数据，最小化静态属性，确保用户及其静态属性的k-匿名性，并通过使用聚合来防止定义的指标充当准标识符，四舍五入,和封顶。数据匿名化需要一种务实的方法，即保持数据的效用，同时最大限度地降低重新识别风险。建议的框架应根据相应数据集的特征进行修改。
