Efficient Federated Kinship Relationship Identification.
Efficient Federated Kinship Relationship Identification.
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发表时间：
2023
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通讯作者：
Xinyu Wang;L. Dervishi;Wentao Li;Xiaoqian Jiang;Erman Ayday;Jaideep Vaidya
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作者：
Xinyu Wang;L. Dervishi;Wentao Li;Xiaoqian Jiang;Erman Ayday;Jaideep Vaidya
亲属关系估计在当今的基因组研究中发挥着重要作用。由于遗传数据大多存储和保护在不同的孤岛中，因此跨联合数据仓库检索所需的亲属关系是一个不小的问题。识别和连接相关个体的能力对于研究和临床应用都很重要。在这项工作中，我们提出了一种新的保护隐私的亲属关系估计框架：增量更新亲属关系识别（INK）。所提出的框架包括三个关键组成部分，使我们能够控制隐私和准确性（亲属关系估计）之间的平衡：增量过程与辅助信息和信息分数的使用相结合。我们的实证评估表明，INK 可以在暴露更少的遗传标记的同时实现更高的亲属关系识别正确性。
Kinship relationship estimation plays a significant role in today's genome studies. Since genetic data are mostly stored and protected in different silos, retrieving the desirable kinship relationships across federated data warehouses is a non-trivial problem. The ability to identify and connect related individuals is important for both research and clinical applications. In this work, we propose a new privacy-preserving kinship relationship estimation framework: Incremental Update Kinship Identification (INK). The proposed framework includes three key components that allow us to control the balance between privacy and accuracy (of kinship estimation): an incremental process coupled with the use of auxiliary information and informative scores. Our empirical evaluation shows that INK can achieve higher kinship identification correctness while exposing fewer genetic markers.