PRINCESS: Privacy-protecting Rare disease International Network Collaboration via Encryption through Software guard extensionS
PRINCESS: Privacy-protecting Rare disease International Network Collaboration via Encryption through Software guard extensionS
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DOI：
10.1093/bioinformatics/btw758
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发表时间：
2017-03-15
期刊：
影响因子：
5.8
通讯作者：
Ohno-Machado, Lucila
中科院分区：
文献类型：
--
作者：
Chen, Feng;Wang, Shuang;Ohno-Machado, Lucila
动机：我们介绍了一个保护隐私的国际合作框架--公主，用于分析分布在不同大陆的罕见疾病基因数据。Princess利用软件保护扩展(SGX)和硬件进行可信计算。与传统的国际协作模式不同，单个级别的患者DNA物理上集中在一个站点，公主在加密数据上执行安全的分布式计算，满足受保护的健康信息的制度政策和法规。结果：为了展示公主的表现和可行性，我们对川崎病进行了基于家庭的等位基因关联研究，数据托管在三个不同的大陆。实验结果表明，与同态加密和乱码电路等其他解决方案相比，Princess提供的安全和准确的分析速度要快得多(速度快40000倍以上)。
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 x faster).