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

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

doi:10.1093/bioinformatics/btw758

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

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

Authors

Feng Chen¹, Shuang Wang¹, Xiaoqian Jiang¹, Sijie Ding¹, Yao Lu², Jihoon Kim¹, S Cenk Sahinalp³, Chisato Shimizu⁴, Jane C Burns⁴, Victoria J Wright⁵, Eileen Png⁶, Martin L Hibberd⁶, David D Lloyd⁷, Hai Yang¹, Amalio Telenti⁸, Cinnamon S Bloss⁹, Dov Fox¹⁰, Kristin Lauter¹¹, Lucila Ohno-Machado¹

Affiliations

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.

Publication types

Multicenter Study

MeSH terms

Computer Security*
Genetic Association Studies / methods*
Genomics / methods
Humans
Mucocutaneous Lymph Node Syndrome / genetics
Privacy*
Rare Diseases / genetics*
Software*

Abstract

Publication types

MeSH terms

Grants and funding