Computational Methods for Enhancing Privacy in Biomedical Data Sharing

增强生物医学数据共享隐私的计算方法

• 批准号: 10478239
• 负责人: Hyunghoon Cho
• 金额: $ 39.28万
• 依托单位: BROAD INSTITUTE, INC.
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-10 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10478239
• 关键词: Address; Adoption; Biological; Biology; Biomedical Research; Clinic; Clinical Data; Code; Collaborations; Communication; Communities; Complex; Complex Analysis; Computational Biology; Computer software; Computing Methodologies; Data; Data Analyses; Data Collection; Data Science; Data Set; Disease; Electronic Health Record; Engineering; Enhancement Technology; Ensure; Foundations; Genome; Genomic medicine; Genomics; Goals; Guidelines; Health; Human; Human Genetics; Image; Individual; Information Theory; Institutes; Institution; Interdisciplinary Study; International; Knowledge; Letters; Machine Learning; Mainstreaming; Mathematics; Medical Genetics; Medical Imaging; Mentorship; Methods; Modernization; Molecular; Nature; Pattern; Pharmacology; Policies; Polynomial Models; Preservation Technique; Privacy; Privatization; Production; Research; Research Personnel; Resources; Risk; Science; Secure; Security; Software Tools; Structure; Techniques; Technology; Translations; Vision; Work; analysis pipeline; base; biomedical data science; brain magnetic resonance imaging; computer framework; computing resources; cost; cryptography; data sharing; design; empowered; experience; experimental study; genetic analysis; genome wide association study; genomic data; infancy; innovation; insight; novel; privacy preservation; privacy protection; software development; statistics; success; task analysis; tool; transcriptome; transcriptomics; web server

Project Summary Data sharing is essential to modern biomedical data science. Access to a large amount of genomic and clinical data can help us better understand human genetics and its impact on health and disease. However, the sensitive nature of biomedical information presents a key bottleneck in data sharing and collection efforts, limiting the utility of these data for science. The goal of this project is to leverage cutting-edge advances in cryptography and information theory to develop innovative computational frameworks for privacy-preserving sharing and analysis of biomedical data. We will draw upon our recent success in developing secure pipelines for collaborative biomedical analyses to address the imminent need to share sensitive data securely and at scale. Practical adoption of existing privacy-preserving techniques in biomedicine has thus far been largely limited due to two major pitfalls, which this project overcomes with novel technical advances. First, emerging cryptographic data sharing frameworks, which promise to enable collaborative analysis pipelines that securely combine data across multiple institutions with theoretical privacy guarantees, are too costly to support complex and large-scale computations required in biomedical analyses. In this project, we will build upon recent advances in cryptography (e.g., secure distributed computation, pseudorandom correlation, zero-knowledge proofs) to significantly enhance the scalability and security of cryptographic biomedical data sharing pipelines. Second, existing approaches that locally transform data to protect sensitive information before sharing (e.g. de-identification techniques) either offer insufficient levels of protection or require excessive perturbation in order to ensure privacy. We will draw upon recent tools from information theory to develop effective local privacy protection methods that achieve superior utility-privacy tradeoffs on a range of biomedical data including genomes, transcriptomes, and medical images by directly exploiting the latent correlation structure of the data. To promote the use of our privacy techniques, we will create production-grade software of our tools and publicly release them. We will also actively participate in international standard-setting organizations in genomics, e.g. GA4GH and ICDA, to incorporate our insights into community guidelines for biomedical privacy. Successful completion of these aims will result in computational methods and software tools that open the door to secure sharing and analysis of massive sets of sensitive genomic and clinical data. Our long-term goal is to broadly enable data sharing and collaboration efforts in biomedicine, thus empowering researchers to better understand the molecular basis of human health and to drive translation of new biological insights to the clinic.

项目概要 数据共享对于现代生物医学数据科学至关重要。访问大量 基因组和临床数据可以帮助我们更好地了解人类遗传学及其对健康的影响 和疾病。然而，生物医学信息的敏感性带来了一个关键瓶颈 在数据共享和收集工作中，限制了这些数据对科学的利用。此举的目标 该项目旨在利用密码学和信息理论的前沿进展来开发 用于生物医学隐私保护共享和分析的创新计算框架 数据。我们将借鉴最近在开发协作安全管道方面取得的成功 生物医学分析，以满足安全和大规模共享敏感数据的迫切需求。 迄今为止，生物医学中现有隐私保护技术的实际采用 由于两个主要缺陷，该项目在很大程度上受到限制，该项目通过新颖的技术克服了这些缺陷 进步。首先，新兴的加密数据共享框架有望实现 协作分析管道可以安全地将多个机构的数据与 理论上的隐私保证，成本太高而无法支持复杂和大规模的计算 生物医学分析中需要。在这个项目中，我们将借鉴以下领域的最新进展： 密码学（例如，安全分布式计算、伪随机相关、零知识 证明）显着增强加密生物医学数据的可扩展性和安全性 共享管道。其次，现有的本地转换数据以保护敏感数据的方法 共享之前的信息（例如去识别技术）要么提供的信息水平不够 保护或需要过度扰动以确保隐私。我们将借鉴最近 信息论工具开发有效的本地隐私保护方法，以实现 对一系列生物医学数据（包括基因组、转录组、 通过直接利用数据的潜在相关结构来分析医学图像。 为了促进我们隐私技术的使用，我们将创建我们的生产级软件 工具并公开发布它们。我们还将积极参与国际标准制定 基因组学组织，例如GA4GH 和 ICDA，将我们的见解融入社区 生物医学隐私指南。成功完成这些目标将导致计算 方法和软件工具为安全共享和分析大量数据打开了大门 敏感的基因组和临床数据。我们的长期目标是广泛实现数据共享和 生物医学领域的合作努力，从而使研究人员能够更好地了解 人类健康的分子基础，并推动新的生物学见解转化为临床。