在医疗数据上的隐私保护搜索
Britta Herres1, Carolin Poschen1, Konstantin Knorr1
1Department of Computer Science, Trier University of Applied Sciences, Trier, Germany.
Studies in health technology and informatics
|August 23, 2024
概括
一个新的隐私保护算法使得安全的医疗数据共享研究. 这种方法通过在分离的数据上使用双阶段搜索来最大限度地降低重新识别风险,从而促进了以假设为导向的研究.
科学领域:
- 医疗信息学 医疗信息学
- 医疗数据安全 医疗数据安全
- 生物信息学是一种生物信息学.
背景情况:
- 促进医学研究需要安全和可访问的数据共享空间.
- 目前的数据共享机制可能无法充分平衡研究人员的访问权限和患者的隐私.
- 欧盟正在积极推动以隐私为主的健康数据共享倡议.
研究的目的:
- 为医疗数据共享提出了一种新的,保护隐私的算法.
- 使研究人员能够访问特定假设的相关数据,同时最大限度地降低重新识别风险.
- 支持开发安全的健康数据共享环境.
主要方法:
- 开发了一个三方,两阶段的搜索算法.
- 该算法运行在集中存储的数据上,但在技术和组织上是分开的.
- 第一阶段检索符合队列标准的数据ID;第二阶段下载数据,如果达到最低队列大小.
主要成果:
- 拟议的算法最大限度地减少了患者重新识别的风险.
- 数据最小化通过分阶段访问方法实现.
- 研究人员独特地连接医疗和人口统计数据,防止恶意第三方访问.
结论:
- 开发的算法为医疗数据共享提供了一个保护隐私的解决方案.
- 这种方法通过提供对敏感信息的受控访问来支持基于假设的研究.
- 它为未来的隐私意识健康数据共享空间铺平了道路,与欧盟提案保持一致.
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