Efficient Federated Kinship Relationship Identification

Authors

Xinyue Wang
Leonard Dervishi
Wentao Li
Xiaoqian Jiang
Erman Ayday
Jaideep Vaidya

Publication Date

1-1-2023

Journal

AMIA Summits on Translational Science Proceedings

Abstract

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.

PMID

37351796

PMCID

PMC10283133

PubMedCentral® Posted Date

6-16-2023

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes