Hammer Freimut, Strufe Thorsten
KASTEL Security Research Labs, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany.
Front Digit Health. 2025 Aug 20;7:1604001. doi: 10.3389/fdgth.2025.1604001. eCollection 2025.
In medical environments, time-continuous data, such as electrocardiographic records, necessitates a distinct approach to anonymization due to the paramount importance of preserving its spatio-temporal integrity for optimal utility. A wide array of data types, characterized by their high sensitivity to the patient's well-being and their substantial interest to researchers, are generated. A significant proportion of this data may be of interest to researchers beyond the original purposes for which it was collected. This necessity underscores the pressing need for effective anonymization methods, a challenge that existing approaches often fail to adequately address. Robust privacy mechanisms are essential to uphold patient rights and ensure informed consent, particularly within the framework of the European Health Data Space. This paper explores the challenges and opportunities inherent in developing a novel approach to anonymize such data and devise suitable metrics to assess the efficacy of anonymization. One promising approach is the adoption of differential privacy to account for temporal context and correlations, making it suitable for time-continuous data.
在医疗环境中,诸如心电图记录等时间连续数据,由于保持其时空完整性对于最佳效用至关重要,因此需要一种独特的匿名化方法。会生成各种各样的数据类型,这些数据类型对患者的健康状况高度敏感,并且对研究人员具有重大意义。其中很大一部分数据可能会引起研究人员的兴趣,超出了其最初收集目的。这种必要性凸显了对有效匿名化方法的迫切需求,而现有方法往往无法充分应对这一挑战。强大的隐私机制对于维护患者权利和确保知情同意至关重要,特别是在欧洲健康数据空间的框架内。本文探讨了开发一种新颖方法对这类数据进行匿名化以及设计合适指标来评估匿名化效果所固有的挑战和机遇。一种有前景的方法是采用差分隐私来考虑时间背景和相关性,使其适用于时间连续数据。