用同型编码的基因型数据进行逻辑和线性回归的保护隐私的模型评估
Seungwan Hong1, Yoolim A Choi1, Daniel S Joo2
1Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA; New York Genome Center, New York, NY 10013, USA.
Journal of biomedical informatics
|June 27, 2024
概括
本研究介绍了一种安全的方法,用于评估使用同态加密的遗传预测模型. 它通过加密所有数据和模型参数来保护患者的隐私,确保分析过程中的保密性.
科学领域:
- 人口遗传学 人口遗传学
- 密码学 密码学 密码学 密码学
- 生物信息学是一种生物信息学.
背景情况:
- 线性回归和逻辑回归对于在人口遗传学中分析大型遗传数据集至关重要.
- 分析敏感的基因型和表型数据引发了严重的患者隐私问题.
- 现有的同型加密方法用于安全计算并不能完全保护共享模型的机密性.
研究的目的:
- 为线性回归和逻辑回归开发一种安全的模型评估方法,保证患者的保密性.
- 解决以前加密方法在保护共享遗传模型方面的局限性.
主要方法:
- 一种使用同态加密的新方法,用于在人口遗传学中安全的模型评估.
- 输入基因型,输出表型和模型参数的加密,以保护隐私.
- 适用于涉及遗传数据分析的六个预测任务.
主要成果:
- 拟议的方法确保在模型推理过程中不会有私人信息泄露.
- 在所有评估的预测任务中都实现了高精度 (≥93%).
- 在大约200个基因组中,每个人推断时间小于10秒.
结论:
- 证明了私人模型评估在人口遗传学中的线性和逻辑回归的可行性.
- 证实了通过理论上的安全保证来保护患者保密的能力.
- 为可重复性和进一步研究提供开源实现和测试数据.
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