医疗环境中时间连续数据的识别和匿名性挑战。

IF 3.2 Q1 HEALTH CARE SCIENCES & SERVICES
Frontiers in digital health Pub Date : 2025-08-20 eCollection Date: 2025-01-01 DOI:10.3389/fdgth.2025.1604001
Freimut Hammer, Thorsten Strufe
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引用次数: 0

摘要

在医疗环境中,时间连续的数据,如心电图记录,需要一种独特的匿名化方法,因为保持其时空完整性对于最佳效用至关重要。产生了一系列广泛的数据类型,其特点是对患者的健康状况高度敏感,并对研究人员产生了浓厚的兴趣。这些数据中的很大一部分可能对研究人员感兴趣,超出了收集这些数据的原始目的。这种必要性强调了对有效匿名化方法的迫切需要,现有方法往往无法充分解决这一挑战。健全的隐私机制对于维护患者权利和确保知情同意至关重要,特别是在欧洲卫生数据空间框架内。本文探讨了开发一种匿名化此类数据的新方法所固有的挑战和机遇,并设计了适当的指标来评估匿名化的有效性。一种有希望的方法是采用差分隐私来解释时间上下文和相关性,使其适合于时间连续数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Challenges of identification and anonymity in time-continuous data from medical environments.

Challenges of identification and anonymity in time-continuous data from medical environments.

Challenges of identification and anonymity in time-continuous data from medical environments.

Challenges of identification and anonymity in time-continuous data from medical environments.

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.

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来源期刊
CiteScore
4.20
自引率
0.00%
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审稿时长
13 weeks
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