Secure outsourced computation of genomic data

基因组数据的安全外包计算

• 批准号: 9906292
• 负责人: Aziz A Boxwala
• 金额: $ 34.49万
• 依托单位: ELIMU INFORMATICS, INC.
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-09 至 2021-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9906292
• 关键词: Age; Algorithms; Alleles; Archives; Awareness; Caring; Client; Clinical; Clinical Data; Clinical Trials; Cloud Computing; Communication; Computer software; Consumption; Data; Data Analyses; Data Protection; Data Reporting; Data Security; Disclosure; Disease; Electronic Health Record; Eligibility Determination; Evolution; Genes; Genetic; Genome; Genomics; Genotype; Goals; Guidelines; Health Sciences; Health care facility; Healthcare; Hospitals; Human; Individual; Investments; Learning; Link; Longevity; Measurement; Measures; Medicine; Memory; Nature; Outsourcing; Patient Data Privacy; Patients; Perception; Performance; Persons; Pharmaceutical Preparations; Pharmacogenomics; Phase; Predisposition; Privacy; Protocols documentation; Recommendation; Research; Rest; Risk; Secure; Security; Services; Small Business Technology Transfer Research; Speed; System; Techniques; Technology; Testing; Texas; Time; Universities; Variant; base; clinical application; clinical care; clinical decision support; clinical decision-making; clinical practice; cloud based; cost efficient; data format; disorder risk; empowered; encryption; genetic variant; genomic data; gigabyte; health care service organization; novel; patient privacy; precision medicine; preservation; privacy protection; response; rural area; software systems; vector; Age; Algorithms; Alleles; Archives; Awareness; Caring; Client; Clinical; Clinical Data; Clinical Trials; Cloud Computing; Communication; Computer software; Consumption; Data; Data Analyses; Data Protection; Data Reporting; Data Security; Disclosure; Disease; Electronic Health Record; Eligibility Determination; Evolution; Genes; Genetic; Genome; Genomics; Genotype; Goals; Guidelines; Health Sciences; Health care facility; Healthcare; Hospitals; Human; Individual; Investments; Learning; Link; Longevity; Measurement; Measures; Medicine; Memory; Nature; Outsourcing; Patient Data Privacy; Patients; Perception; Performance; Persons; Pharmaceutical Preparations; Pharmacogenomics; Phase; Predisposition; Privacy; Protocols documentation; Recommendation; Research; Rest; Risk; Secure; Security; Services; Small Business Technology Transfer Research; Speed; System; Techniques; Technology; Testing; Texas; Time; Universities; Variant; base; clinical application; clinical care; clinical decision support; clinical decision-making; clinical practice; cloud based; cost efficient; data format; disorder risk; empowered; encryption; genetic variant; genomic data; gigabyte; health care service organization; novel; patient privacy; precision medicine; preservation; privacy protection; response; rural area; software systems; vector

Project Summary In the age of precision medicine, genomic data are being integrated with other health care data to support personalized and calibrated clinical decision-making. Genomic sequence data are too large to be stored in electronic health record (EHR) systems and need to be separately stored. While cloud computing offers a cost-efficient and scalable platform, the privacy and security concerns about outsourcing genomic data are challenging issues. The common perception is that the ease of access to remote data and the protection of privacy are at odds with each other. We propose a new genomics archiving and communications system (GACS) that meets both requirements by using state-of-the-art homomorphic encryption algorithms and matrix representation of data and queries. In this system, variants are represented as vectors, that are homomorphically encrypted by a client and stored on the GACS server. When analysis is required, a query is generated in the form of a matrix. This matrix is encrypted (or can remain in plaintext depending on the task) and sent to the GACS server. The server computes on encrypted data, produces an encrypted result and returns it to the client, who has the secret key to decode it. The GACS is not able to decrypt the data or the encrypted queries, thus guaranteeing that privacy and security are maintained on the GACS. Preliminary results of the algorithms show that after decryption, the results are the same as results from computing on plaintext. In this project, we will implement our GACS system software modules and demonstrate the use of the system with examples from three use- cases: pharmacogenomics, clinical trials eligibility and analysis for disease risks. We will measure performance speed and memory consumption in all three use-cases. A GACS system as a cloud-hosted service can reduce the computational burden on healthcare facilities. It can provide small healthcare facilities with the same genomic analysis capability available to larger hospitals. In addition, clinical decision support (CDS) can be deployed on the GACS. As clinical guidelines evolve in response to new discoveries linking genetic variants to disease and medicines, healthcare facilities can stay in compliance with the guidelines.

项目摘要 在精确医学时代，基因组数据正在与其他医疗保健数据集成 支持个性化和校准的临床决策。基因组序列数据也是 大量存储在电子健康记录（EHR）系统中，需要单独存储。 尽管云计算提供了一个具有成本效益且可扩展的平台，但隐私和安全性 对外包基因组数据的担忧是具有挑战性的问题。普遍的看法是 易于访问远程数据和隐私保护的易用性相互矛盾。 我们提出了一个新的基因组学归档和通信系统（GACS） 使用最先进的同型加密算法和矩阵的要求 数据和查询的表示。 在此系统中，变体被表示为向量，这些向量由 客户端并存储在GACS服务器上。当需要分析时，将在 矩阵的形式。此矩阵是加密的（或可以根据任务保留在明文中），并且 发送到GACS服务器。服务器计算加密数据，产生加密结果 并将其退还给客户，后者具有将其解码的秘密键。 GACS无法解密 数据或加密查询，从而确保维护隐私和安全性 在Gacs上。算法的初步结果表明，解密后，结果是 与纯文本计算的结果相同。在这个项目中，我们将实施我们的GACS 系统软件模块并演示了系统的使用，其中三个使用的示例 - 病例：药物基因组学，临床试验资格和疾病风险分析。我们将 测量所有三个用例中的性能速度和内存消耗。 GACS系统作为云托管服务可以减轻医疗保健的计算负担 设施。它可以提供具有相同基因组分析能力的小型医疗机构 可用于大型医院。此外，临床决策支持（CD）可以部署 GACS。随着临床指南的发展，响应将遗传变异与 疾病和药物，医疗机构可​​以符合准则。