一个框架,用于对大量多方基因组数据的隐私保护相似性搜索
Jia Liu1,2, Yanping Xu1,2, Abdullah Gera1,2
1Office of Academic Research, Weifang Institute of Technology, Weifang, China.
Frontiers in plant science
|December 19, 2025
概括
这项研究介绍了Fed-LSH,这是一个保护隐私的合作作物育种框架. 它使跨机构的基因组分析能够在不共享原始数据的情况下进行,从而增强精英等位基因的识别.
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.
- 计算生物学 计算生物学
背景情况:
- 数据孤岛和隐私问题阻碍了在作物育种中进行多机构基因组数据分析.
- 识别精英等位基因和类似的基因组关联需要在各种数据集中安全匹配数据.
研究的目的:
- 提出Fed-LSH框架,将隐私增强的局部敏感哈希 (LSH) 与联合学习集成在一起.
- 实现跨机构的基因组关联分析和精英等位基因鉴定,而无需共享原始数据.
主要方法:
- 使用一个协作框架,结合了局部敏感哈希 (LSH) 和联合学习.
- 使用分布式哈希索引构建,外包计算和加密相似性搜索.
- 在标准个人电脑上实现Fed-LSH,用于性能评估.
主要成果:
- 在从3072维数据推4个候选人时,达到60.72%±1.2%的命中率.
- 在不到0.5秒的时间内处理10,000个基因组片段 (3072维) 进行候选人选择.
- 展示了有效和准确的性能指标,用于保护隐私的基因组分析.
结论:
- 联邦-LSH为保护隐私的合作作物育种提供了基础的技术支持.
- 该框架有效地解决了多方基因组数据匹配中的数据孤岛和隐私问题.
- 能够在各个机构中安全识别精英等位基因和类似的基因组关联.
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