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.

随着在生物医学研究中收集的数据量的快速增长（例如人类基因组数据），数据保护，特别是对于患者在这些数据的二次用途中的隐私，已经吸引了很多最近注意。如今，绝大多数敏感的生物医学数据，包括个体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必须签署用户协议访问这些数据。安全研究已经生产了一套可以提供保护隐私计算的一般目的；但是，他们的直接申请是对于实际生物医学应用而言，太昂贵（在资源消耗方面）。另一种解决方案是已开发了硬件辅助的可信执行环境（TEE）解决方案或由硬件供应商（英特尔，AMD，ARM）和开源研究开发社区。一个突出的例子是英特尔的软件守卫扩展名（SGX），可作为英特尔主流CPU（即Skylake和Kaby Lake）中的特征。在这个项目中，我们计划探索 TEE在两个流行的基因组计算任务上的潜在应用涉及敏感的生物医学数据，即全基因组和全球范围的关联研究。对于二级研究GWAS 用户可以使用（情况）或没有（对照）A疾病收集基因组序列（以加密形式）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SGX飞地内进行。同样，对于Phewas，用户可以从含有（病例）或不包含（对照）一个或多个特定变异的个体。开发这些方法时，我们将解决两个问题：1）我们将自定义GWAS/PHEWAS 在TEE中有限的资源（例如，内存，I/O等）中有效执行的算法，以及2）我们将开发新的基因组计算外包和提起SGX技术的数据共享平台，并进一步了解并减轻有关GWAS/PHEWAS的潜在侧向通道风险计算任务。拟议的研究将为安全GWA和Phewas提供实用的解决方案在三种应用方案中：1）安全外包：研究机构收集匹配的基因组以及来自大量案件和控制个人的表型数据，并将存储外包这些数据以及潜在的重复GWAS和PHEWAS计算对公共或商业云； 2）安全合作：多个机构的研究人员联盟试图合作在大型GWAS/PHEWAS研究中，使用每个参与机构收集的数据； 3）安全数据共享：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因此，潜在的数据使用者可以进行次要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.