同型加密可以保护隐私,保护多基因风险得分
Elizabeth Knight1, Jiaqi Li1, Matthew Jensen1
1Program in Computational Biology and Biomedical Informatics, Yale University, New Haven, CT 06520, USA.
Cell reports methods
|January 9, 2026
概括
完全同型加密 (FHE) 能够为个性化医疗提供保护隐私的多基因风险评分模型 (PRS). 我们的HEPRS工具展示了实用,准确和安全的基因组风险预测,而不会损害敏感的患者数据.
科学领域:
- 基因组学就是基因组学.
- 密码学 密码学 密码学 密码学
- 精准医学是一门精准的医学.
背景情况:
- 多基因风险评分 (PRS) 对于准确医学中个性化风险预测至关重要.
- 由于基因组数据的敏感性,PRS的使用引发了严重的隐私问题.
研究的目的:
- 为保护隐私的多基因风险评分 (PRS) 模型开发和评估完全同型加密 (FHE) 的开源实现.
- 评估使用FHE用于安全基因组数据计算的可行性和准确性.
主要方法:
- 开发了HEPRS,这是一个开源系统,利用基于FHE的PRS的三方模型 (客户,建模者,评估者).
- 将HEPRS应用于合成数据集和11万个SNP精神分裂症风险模型.
- 研究了影响准确性,内存和计算时间的加密参数.
主要成果:
- 加密和纯文本PRS显示出密切一致,表明高准确性.
- HEPRS在单个CPU上展示了实际的可用性.
- 该研究证实,FHE能够实现真实的PRS,而准确性损失微不足道.
结论:
- 完全同态加密为保护隐私的PRS提供了一个可行的解决方案.
- HEPRS支持安全和可扩展的基因组分析,增强精准医学应用.
- 开发的FHE实现方便从敏感的基因组数据进行机密风险预测.
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