Computational Methods for Enhancing Privacy in Biomedical Data Sharing

项目摘要 数据共享对于现代生物医学数据科学至关重要。免费提供海量 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是一个关键瓶颈 在数据共享和收集工作中，限制了这些数据对科学的效用。这个目标 该项目是利用密码学和信息理论的前沿进展， 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要。 到目前为止，在生物医学中实际采用现有的隐私保护技术， 由于两个主要缺陷，该项目在很大程度上受到限制， 预付款。第一，新兴的加密数据共享框架，承诺使 协作分析管道，安全地将多个机构的数据联合收割机与 理论上的隐私保证，对于支持复杂和大规模的计算来说， 生物医学分析所需的。在这个项目中，我们将建立在最近的进展， 密码学（例如，安全分布式计算，伪随机相关，零知识 证明），以显著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，现有的方法，本地转换数据，以保护敏感的 共享前的信息（例如去识别技术）要么提供的信息水平不足， 保护或需要过度干扰以确保隐私。我们将利用最近 从信息理论的工具，以制定有效的本地隐私保护方法，实现 对一系列生物医学数据进行上级实用性-隐私性权衡，包括基因组、转录组、 和医学图像。 为了促进我们的隐私技术的使用，我们将创建生产级软件， 工具并公开发布。我们还将积极参与国际标准制定 基因组学组织，如GA 4GH和ICDA，将我们的见解融入社区 生物医学隐私准则。成功完成这些目标将导致计算 方法和软件工具，打开大门，以安全共享和分析大量的 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， 在生物医学方面的合作努力，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.