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; +9 more... Eileen Png; Martin L Hibberd; David D Lloyd; Hai Yang; Amalio Telenti; Cinnamon S Bloss; Dov Fox; Kristin Lauter; Lucila Ohno-Machado; (2016) PRINCESS: Privacy-protecting Rare disease International Network Collaboration via Encryption through Software guard extensionS. Bioinformatics (Oxford, England), 33 (6). pp. 871-878. ISSN 1367-4803 DOI: 10.1093/bioinformatics/btw758
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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.

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