Realizing privacy preserving genome-wide association studies
Name
Berger_Realizing privacy.pdf
Size
392.33 KB
Format
Adobe PDF
Checksum (MD5)
70b9159e19d06de725b3fff46db4e80e
Author(s)
Simmons, Sean Kenneth
Berger Leighton, Bonnie
Date Issued
January 2016
Journal
Bioinformatics
Publisher
Oxford University Press
Citation
Simmons, Sean, and Bonnie Berger. “Realizing Privacy Preserving Genome-Wide Association Studies.” Bioinformatics 32, no. 9 (January 14, 2016): 1293-1300.
Version
Final published version
Abstract
Motivation: As genomics moves into the clinic, there has been much interest in using this medical data for research. At the same time the use of such data raises many privacy concerns. These circumstances have led to the development of various methods to perform genome-wide association studies (GWAS) on patient records while ensuring privacy. In particular, there has been growing interest in applying differentially private techniques to this challenge. Unfortunately, up until now all methods for finding high scoring SNPs in a differentially private manner have had major drawbacks in terms of either accuracy or computational efficiency.
Results: Here we overcome these limitations with a substantially modified version of the neighbor distance method for performing differentially private GWAS, and thus are able to produce a more viable mechanism. Specifically, we use input perturbation and an adaptive boundary method to overcome accuracy issues. We also design and implement a convex analysis based algorithm to calculate the neighbor distance for each SNP in constant time, overcoming the major computational bottleneck in the neighbor distance method. It is our hope that methods such as ours will pave the way for more widespread use of patient data in biomedical research.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Mathematics
Terms of Use
Creative Commons Attribution 4.0 International License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1093/bioinformatics/btw009
