Computational Methods for Enhancing Privacy in Biomedical Data Sharing

项目摘要 数据共享是现代生物医学数据科学的重要组成部分。获取大量的 基因组和临床数据可以帮助我们更好地了解人类基因及其对健康的影响 和疾病。然而，生物医学信息的敏感性是一个关键的瓶颈。 在数据共享和收集工作中，限制了这些数据对科学的效用。这样做的目的是 该项目是利用密码学和信息理论的尖端进展来开发 用于生物医学隐私保护共享和分析的创新计算框架 数据。我们将利用我们最近在开发安全管道方面的成功，为协作 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。 到目前为止，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，该项目通过新颖的技术克服了这两个缺陷，这在很大程度上是有限的 预付款。首先，新兴的加密数据共享框架承诺实现 协作分析管道，可将多个机构的数据安全地结合在一起 理论上的隐私保证成本太高，无法支持复杂和大规模的计算 生物医学分析中所需的。在这个项目中，我们将在以下方面取得最新进展 密码学(例如，安全分布式计算、伪随机相关、零知识 证据)以显著提高加密生物医学数据的可扩展性和安全性 共享管道。第二，现有方法在本地转换数据以保护敏感数据 共享前的信息(例如身份识别技术)要么提供的信息不足以 保护或要求过度干扰，以确保隐私。我们将利用最近的 来自信息理论的工具来开发有效的本地隐私保护方法，以实现 在一系列生物医学数据(包括基因组、转录本、 通过直接利用数据的潜在相关性结构，对医学图像进行分类。 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的制定。 基因组学组织，例如GA4GH和ICDA，将我们的见解纳入社区 生物医学隐私指南。这些目标的成功实现将导致计算 为安全共享和分析海量集合打开大门的方法和软件工具 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，并推动将新的生物学见解转化为临床。

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.