Privacy-preserving federated genome-wide association studies via dynamic sampling.

ABSTRACT:

Motivation

Genome-wide association studies (GWAS) benefit from the increasing availability of genomic data and cross-institution collaborations. However, sharing data across institutional boundaries jeopardizes medical data confidentiality and patient privacy. While modern cryptographic techniques provide formal secure guarantees, the substantial communication and computational overheads hinder the practical application of large-scale collaborative GWAS.

Results

This work introduces an efficient framework for conducting collaborative GWAS on distributed datasets, maintaining data privacy without compromising the accuracy of the results. We propose a novel two-step strategy aimed at reducing communication and computational overheads, and we employ iterative and sampling techn

SUBMITTER: Wang X 

PROVIDER: S-EPMC10612407 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature