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Bioinformatics. 2017 Mar 15;33(6):871-878. doi: 10.1093/bioinformatics/btw758.

PRINCESS: Privacy-protecting Rare disease International Network Collaboration via Encryption through Software guard extensionS.

Author information

1
Health System Department of Biomedical Informatics, University of California San Diego, La Jolla, CA, USA.
2
Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA.
3
Department of Computer Science and Informatics, Indiana University, Bloomington, IN, USA.
4
Department of Pediatrics, University of California San Diego, La Jolla, CA, USA.
5
Section of Pediatrics, Imperial College London, London, UK.
6
Genome Institute of Singapore, ASTAR, Singapore, Singapore.
7
Deparment of Pediatrics, School of Medicine, Emory University, Atlanta, GA, USA.
8
J. Craig Venter Institute, La Jolla, CA, USA.
9
Department of Psychiatry, University of California San Diego, La Jolla, CA, USA.
10
School of Law, University of San Diego, San Diego, CA, USA.
11
Cryptography Group, Microsoft Research, San Diego, CA, USA.

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.

PMID:
28065902
PMCID:
PMC5860394
DOI:
10.1093/bioinformatics/btw758
[Indexed for MEDLINE]
Free PMC Article

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