1. PRINCESS: Privacy-protecting Rare disease International Network Collaboration via Encryption through Software guard extensionS.
- Author
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Chen F, Wang S, Jiang X, Ding S, Lu Y, Kim J, Sahinalp SC, Shimizu C, Burns JC, Wright VJ, Png E, Hibberd ML, Lloyd DD, Yang H, Telenti A, Bloss CS, Fox D, Lauter K, and Ohno-Machado L
- Subjects
- Genomics methods, Humans, Mucocutaneous Lymph Node Syndrome genetics, Computer Security, Genetic Association Studies methods, Privacy, Rare Diseases genetics, Software
- Abstract
Motivation: We introduce PRINCESS, a privacy-preserving international collaboration framework for analyzing rare disease genetic data that are distributed across different continents. PRINCESS leverages Software Guard Extensions (SGX) and hardware for trustworthy computation. Unlike a traditional international collaboration model, where individual-level patient DNA are physically centralized at a single site, PRINCESS performs a secure and distributed computation over encrypted data, fulfilling institutional policies and regulations for protected health information., Results: To demonstrate PRINCESS' performance and feasibility, we conducted a family-based allelic association study for Kawasaki Disease, with data hosted in three different continents. The experimental results show that PRINCESS provides secure and accurate analyses much faster than alternative solutions, such as homomorphic encryption and garbled circuits (over 40 000× faster)., Availability and Implementation: https://github.com/achenfengb/PRINCESS_opensource., Contact: shw070@ucsd.edu., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com)
- Published
- 2017
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