Facilitating Federated Genomic Data Analysis by Identifying Record Correlations while Ensuring Privacy

Authors

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

Publication Date

1-1-2022

Journal

AMIA Annual Symposium Proceedings

Abstract

With the reduction of sequencing costs and the pervasiveness of computing devices, genomic data collection is continually growing. However, data collection is highly fragmented and the data is still siloed across different repositories. Analyzing all of this data would be transformative for genomics research. However, the data is sensitive, and therefore cannot be easily centralized. Furthermore, there may be correlations in the data, which if not detected, can impact the analysis. In this paper, we take the first step towards identifying correlated records across multiple data repositories in a privacy-preserving manner. The proposed framework, based on random shuffling, synthetic record generation, and local differential privacy, allows a trade-off of accuracy and computational efficiency. An extensive evaluation on real genomic data from the OpenSNP dataset shows that the proposed solution is efficient and effective.

Keywords

Humans, Privacy, Computer Security, Genomics, Data Collection

PMID

37128365

PMCID

PMC10148342

PubMedCentral® Posted Date

4-29-2023

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes