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评估遗传数据集中的隐私漏洞:范围审查
Mara Thomas1, Nuria Mackes2, Asad Preuss-Dodhy3
1F. Hoffmann-La Roche AG, Basel, Switzerland.
JMIR bioinformatics and biotechnology
|June 27, 2024
概括
评估遗传数据隐私风险是复杂的. 本研究确定了遗传数据的9个关键特征,以指导数据处理人员评估重新识别漏洞.
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
- 数据 隐私 数据 隐私 数据
背景情况:
- 遗传数据通常被认为是固有的可识别性,造成重大隐私风险.
- 评估遗传数据集的重新识别风险是复杂的,因为数据的特征有所不同.
- 对数据处理者来说,缺乏明确的指导方针来评估遗传数据的隐私.
研究的目的:
- 为了全面了解与遗传数据相关的隐私漏洞.
- 制定一个框架,指导数据处理者评估遗传数据隐私风险.
- 总结影响遗传数据集重新识别潜力的关键特征.
主要方法:
- 进行了两步文献搜索,确定了21篇关于基因组隐私的评论文章.
- 分析了这些评论 (n=1645) 的参考文献,发现了42项关于隐私攻击的原始研究.
- 评估了被利用的遗传数据类型,所需的资源以及隐私攻击的成功率.
主要成果:
- 鉴定出了九个基因数据的非相互排斥的特征,这些特征对于隐私风险评估至关重要.
- 这些特征包括生物模式,试验类型,数据格式,生殖系/体质变异,SNP含量,STR,聚合措施,结构变异和罕见变异.
- 这些特征是遗传数据集的固有特征,并对它们的重新识别潜力提供了信息.
结论:
- 识别的9个特征为评估遗传数据隐私风险提供了基础.
- 评估这些特征涵盖了遗传数据的大部分隐私关键方面.
- 该框架为数据处理人员提供指导,以系统地评估遗传数据集的隐私.
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