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

Bioinformatics
Feng ChenLucila Ohno-Machado

Abstract

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. 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). https://github.com/achenfengb/PRINCESS_opensource. shw070@ucsd.edu. Supplementary data are available at Bioinformatics online.

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Feb 6, 2020·JMIR Medical Informatics·Mengchun GongZhouguang Hui
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Apr 6, 2017·Bioinformatics·Zhicong HuangJean-Pierre Hubaux
Jul 20, 2020·Bioinformatics·Mete AkgünOliver Kohlbacher
Nov 11, 2019·Christophe DessimozBristena Oprisanu
Nov 11, 2019·Christian WeinertThomas Schneider

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