Private Random Variate Sampling for Secure and Federated Polygenic Risk Scores
Name
yen-djwyen-meng-eecs-2024-thesis.pdf
Description
Thesis PDF
Size
5.77 MB
Format
Adobe PDF
Checksum (MD5)
6a3fe8a42413c33e5997f5dfcabb5de4
Author(s)
Yen, Derek Jia-Wen
Advisor(s)
Berger, Bonnie
Cho, Hyunghoon
Date Issued
February 2024
Publisher
Massachusetts Institute of Technology
Abstract
Polygenic risk scores (PRS) are used to quantify the additive effect of single nucleotide polymorphisms (SNPs) on an individual’s genetic risk for developing a particular trait or condition. Collaborations between data centers are important for improving the statistical power and validity of PRS through larger, more genetically diverse datasets. However, owing to the privacy concerns inherent in genomic data, regulations restrict institutions’ capacity to share data. Using cryptography, we present a secure and federated implementation of a Monte Carlo algorithm for PRS, enabling collaborations that respect data regulations. To implement a Monte Carlo algorithm in a privacy-preserving context, our work exhibits techniques for sampling random variates with cryptographically private parameters, which may be of independent interest.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Copyright retained by author(s)
Persistent DSpace Link
