Chen Feng, Wang Shuang, Jiang Xiaoqian, Ding Sijie, Lu Yao, Kim Jihoon, Sahinalp S Cenk, Shimizu Chisato, Burns Jane C, Wright Victoria J, Png Eileen, Hibberd Martin L, Lloyd David D, Yang Hai, Telenti Amalio, Bloss Cinnamon S, Fox Dov, Lauter Kristin, Ohno-Machado Lucila
Health System Department of Biomedical Informatics, University of California San Diego, La Jolla, CA, USA.
Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA.
Bioinformatics. 2017 Mar 15;33(6):871-878. doi: 10.1093/bioinformatics/btw758.
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.
Supplementary data are available at Bioinformatics online.
我们推出了PRINCESS，这是一个用于分析分布在不同大陆的罕见病基因数据的隐私保护国际合作框架。PRINCESS利用软件防护扩展（SGX）和硬件进行可信计算。与传统的国际合作模式不同，在传统模式中个体水平的患者DNA会实际集中在一个单一地点，而PRINCESS对加密数据进行安全的分布式计算，符合关于受保护健康信息的机构政策和法规。
为了证明PRINCESS的性能和可行性，我们针对川崎病进行了一项基于家系的等位基因关联研究，数据存储在三个不同的大陆。实验结果表明，PRINCESS提供安全且准确的分析，比诸如同态加密和混淆电路等替代解决方案快得多（快超过40000倍）。
https://github.com/achenfengb/PRINCESS_opensource。
补充数据可在《生物信息学》在线获取。
