Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Center of Health Data Science, Charitéplatz 1, 10117 Berlin, Germany.
Stud Health Technol Inform. 2024 Aug 22;316:1199-1203. doi: 10.3233/SHTI240626.
Sharing biomedical data for research can help to improve disease understanding and support the development of preventive, diagnostic, and therapeutic methods. However, it is vital to balance the amount of data shared and the sharing mechanism chosen with the privacy protection provided. This requires a detailed understanding of potential adversaries who might attempt to re-identify data and the consequences of their actions. The aim of this paper is to present a comprehensive list of potential types of adversaries, motivations, and harms to targeted individuals. A group of 13 researchers performed a three-step process in a one-day workshop, involving the identification of adversaries, the categorization by motivation, and the deduction of potential harms. The group collected 28 suggestions and categorized them into six types, each associated with several of six distinct harms. The findings align with previous efforts in structuring threat actors and outcomes and we believe that they provide a robust foundation for evaluating re-identification risks and developing protection measures in health data sharing scenarios.
为研究而共享生物医学数据有助于增进对疾病的认识，并支持预防、诊断和治疗方法的开发。然而，至关重要的是，需要在共享的数据量和所选择的共享机制与提供的隐私保护之间取得平衡。这需要详细了解可能试图重新识别数据的潜在对手以及他们行为的后果。本文的目的是提出一个全面的潜在对手类型、动机和针对个人的危害清单。一组 13 名研究人员在为期一天的研讨会上进行了三步流程，包括识别对手、按动机进行分类以及推断潜在危害。该小组收集了 28 条建议，并将其分为六种类型，每种类型都与六种不同危害中的几种相关。研究结果与之前在构建威胁行为者和结果方面的努力一致，我们相信它们为评估重新识别风险和在健康数据共享场景中制定保护措施提供了坚实的基础。
