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Title: Efficient Federated Kinship Relationship Identification.
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.  more » « less
Award ID(s):
2124789
NSF-PAR ID:
10448808
Author(s) / Creator(s):
; ; ; ; ;
Date Published:
Journal Name:
AMIA Jt Summits Transl Sci Proc 2023
Page Range / eLocation ID:
534–543
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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