权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Secure and Privacy-preserving Genome-wide and Phenome-wide Association Studies via Intel Software Guard Extensions (SGX)

通过英特尔软件防护扩展 (SGX) 进行安全且保护隐私的全基因组和全表型关联研究

基本信息

批准号：
10470341
负责人：
HAIXU TANG
金额：
$ 34.06万
依托单位：
TRUSTEES OF INDIANA UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-08-09 至 2024-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10470341
关键词：
Address Administrator Agreement Algorithms Attention Beds Biomedical Computing Biomedical Research Code Collaborations Communities Computer software Consumption Custom Data Data Analyses Data Protection Data Security Data Storage and Retrieval Databases Disease Environment Genome Genomics Health Human Individual Industrialization Institution Mainstreaming Memory Metadata Methods Operating System Outsourcing Patients Phenotype Privacy Privatization Process Research Research Personnel Resources Retrieval Risk Running Secondary to Secure Security Side Stream System Techniques Technology Testing Trust Variant Vendor base case control cohort computer studies cryptography data dissemination data format data sharing database of Genotypes and Phenotypes design direct application disease phenotype encryption genome wide association study genome-wide genomic data human genomics improved indexing interest large datasets novel open source patient privacy phenome phenotypic data privacy preservation rapid growth scale up screening secondary analysis sharing platform transmission process vector whole genome

项目摘要

With the rapid growth of the data volume (e.g., human genomic data) collected in biomedical research, data protection, in particular for patients’ privacy in secondary uses of these data, has attracted much attention recently. Today, a vast majority of sensitive biomedical data, including individual human genomic data and their associated health metadata, are shared only through controlled-access databases (e.g. dbGaP) and biomedical researchers are required to sign a user agreement before getting access to these data. Security research has already produced a suite of techniques that can serve the general purpose of privacy-preserving computation; their direct applications are, however, too expensive (in terms of resource consumption) for real-world biomedical applications. An alternative solution is hardware-assisted Trusted Execution Environment (TEE) solutions developed or being developed by both hardware vendors (Intel, AMD, ARM) and the open-source research community. A prominent example is Intel’s Software Guard Extension (SGX), which is available as a feature in Intel's mainstream CPUs (i.e., Skylake and Kaby Lake). In this project, we plan to explore potential applications of TEE to two popular genome computation tasks involving sensitive biomedical data, i.e., the genome-wide and phenome-wide association studies. For GWAS, a secondary research user may collect genomic sequences (in encrypted form) with (cases) or without (controls) a disease phenotype from multiple data owners, on which association tests or advanced GWAS algorithms can be conducted within the SGX enclave. Similarly, for PheWAS, a user may collect phenotype data from individuals whose genomes containing (case) or not containing (control) one or more specific variations. We will address two issues when developing these approaches: 1) we will customize GWAS/PheWAS algorithms for efficient execution in the TEE with limited resources (e.g, memory, I/O, etc), and 2) we will develop new genome computing outsourcing and data sharing platforms suing the SGX techniques, and further understand and mitigate its potential side-channel risks with regards to GWAS/PheWAS computing tasks. The proposed research will lead to a practical solution for secure GWAS and PheWAS in three application scenarios: 1) secure outsourcing: a research institution collects matched genomic and phenotypic data from a large cohort of case and control individuals, and outsources the storage of these data and potential repeated GWAS and PheWAS computation to a public or commercial cloud; 2) secure collaboration: a consortium of researchers across multiple institutions attempt to collaborate on a large GWAS/PheWAS study using the data collected by each participating institution; and 3) secure data sharing: researchers want to share their data with a broad biomedical research community so that potential data users may conduct a secondary GWAS/PheWAS analysis.

随着生物医学研究中收集的数据量(例如，人类基因组数据)的快速增长，数据保护，特别是在这些数据的二次使用中保护患者的隐私，已经引起了人们的极大关注最近请注意。今天，绝大多数敏感的生物医学数据，包括个人人类基因组数据及其相关的健康元数据仅通过受控访问共享数据库(例如，DBGaP)和生物医学研究人员在签署用户协议之前获取这些数据。安全研究已经产生了一套技术，可以服务于隐私保护计算的一般目的；然而，它们的直接应用是，对于现实世界的生物医学应用来说，成本太高(就资源消耗而言)。另一种解决方案是开发的硬件辅助可信执行环境(TEE)解决方案或由硬件供应商(英特尔、AMD、ARM)和开源研究共同开发社区。一个突出的例子是英特尔的软件保护扩展(SGX)，它以在英特尔的主流CPU(即Skylake和Kaby Lake)中使用。在这个项目中，我们计划探索 TEE在两个涉及敏感生物医学的流行基因组计算任务中的潜在应用数据，即全基因组和全表型的关联研究。对于GWAS来说，这是一项次要研究用户可以收集有(病例)或无(对照)疾病的(加密形式)基因组序列来自多个数据所有者的表型，关联测试或高级GWAS算法可以在新加坡交易所飞地内进行。类似地，对于Phewas，用户可以从基因组包含(病例)或不包含(对照)一个或多个特定变异的个体。在开发这些方法时，我们将解决两个问题：1)我们将定制GWAS/PheWAS 在具有有限资源(例如，内存、I/O等)的TEE中高效执行的算法，以及2)我们将开发新的基因组计算外包和数据共享平台，采用SGX技术，并进一步了解和缓解其潜在的与GWAs/PheWAs相关的旁路风险计算任务。所提出的研究将导致一个实用的解决方案，为安全的GWAs和PheWAs 在三个应用场景中：1)安全外包：研究机构收集匹配的基因组和大量病例和对照个体的表型数据，并将存储将这些数据和潜在的重复GWA值和PheWA值计算到公共云或商业云中； 2)安全协作：多个机构的研究人员联盟试图进行协作使用每个参与机构收集的数据进行的一项大型GWAS/Phewas研究；以及3) 安全的数据共享：研究人员希望与广泛的生物医学研究社区共享他们的数据以便潜在的数据用户可以进行二次GWAS/Phewas分析。

项目成果

期刊论文数量（9）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Haplotype-based membership inference from summary genomic data.

DOI：
10.1093/bioinformatics/btab305
发表时间：
2021-07-12
期刊：
Bioinformatics (Oxford, England)
影响因子：
0
作者：
Bu D;Wang X;Tang H
通讯作者：
Tang H

A Fast, Provably Accurate Approximation Algorithm for Sparse Principal Component Analysis Reveals Human Genetic Variation Across the World

稀疏主成分分析的快速、可证明准确的近似算法揭示了世界各地的人类遗传变异

DOI：
10.1007/978-3-031-04749-7_6
发表时间：
2022
期刊：
Research in Computational Molecular Biology - 26th Annual International Conference
影响因子：
0
作者：
Chowdhury, Agniva;Bose, Aritra;Zhou, Samson;Woodruff, David P.;Drineas, Petros
通讯作者：
Drineas, Petros

Hutch++: Optimal Stochastic Trace Estimation.