PRINCESS: Privacy-protecting Rare disease International Network Collaboration via Encryption through Software guard extensionS
File(s)
Accepted version
Author(s)
Type
Journal Article
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).
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).
Date Issued
2016-12-22
Date Acceptance
2016-11-23
Citation
BIOINFORMATICS, 2016, 33 (6), pp.871-878
ISSN
1367-4803
Publisher
Oxford University Press
Start Page
871
End Page
878
Journal / Book Title
BIOINFORMATICS
Volume
33
Issue
6
Copyright Statement
© 2016 The Author(s). Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com. This is a pre-copy-editing, author-produced version of an article accepted for publication in Bioinformatics following peer review. The definitive publisher-authenticated version Feng Chen, Shuang Wang, Xiaoqian Jiang, Sijie Ding, Yao Lu, Jihoon Kim, S. Cenk Sahinalp, Chisato Shimizu, Jane C. Burns, Victoria J. Wright, Eileen Png, Martin L. Hibberd, David D. Lloyd, Hai Yang, Amalio Telenti, Cinnamon S. Bloss, Dov Fox, Kristin Lauter, Lucila Ohno-Machado; PRINCESS: Privacy-protecting Rare disease International Network Collaboration via Encryption through Software guard extensionS. Bioinformatics 2017; 33 (6): 871-878. doi: 10.1093/bioinformatics/btw758 is available online at: https://dx.doi.org/10.1093/bioinformatics/btw758
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000397986500010&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Subjects
Science & Technology
Life Sciences & Biomedicine
Technology
Physical Sciences
Biochemical Research Methods
Biotechnology & Applied Microbiology
Computer Science, Interdisciplinary Applications
Mathematical & Computational Biology
Statistics & Probability
Biochemistry & Molecular Biology
Computer Science
Mathematics
GENOME-WIDE ASSOCIATION
LOGISTIC-REGRESSION
KAWASAKI-DISEASE
GWAS
COMPUTATION
POPULATION
RELATIVES
DATASETS
Bioinformatics
01 Mathematical Sciences
06 Biological Sciences
08 Information And Computing Sciences
Publication Status
Published