Protecting Privacy and Facilitating Shared Access of Clinical and Genetic Data of Special Populations

项目总结 强心研究(SHS)是一项多中心的纵向资源，旨在更好地了解 美国印第安人的心血管疾病，确定重要的危险因素，促进新的研究和交付 更好的医疗保健。为了实现这些目标，有兴趣和有资格的人应该能够访问SHS数据 研究人员，而不会对贡献数据的研究参与者造成伤害。因此，私人信息 在数据和参与者身份方面应得到保护，并与SHS部落主权和协议 这包括对所有社会科学及人文科学数据使用请求的部落审查和批准。我们的研究旨在 使用先进技术和科学计算工具包解决这些问题，以实现共享，但 受保护的数据访问，以及了解社会科学及人文科学参与者的数据共享偏好。第一 目标是开发一种创新的、安全的以数据为中心的服务，以保护SHS数据上的计算 参与的SHS部落、SHS调查人员和NIH可以接受的治理实践。 具体地说，我们将建立一个系统，通过虚拟专用网络对受保护的数据进行安全分析， 只有经过批准的操作和输出才被允许。拟议的框架将允许研究人员 轻松、安全地对SHS数据和荟萃分析执行特定的统计分析。第二个目标是 开发新的联合计算模型，以支持SHS协调中心和遗传中心 以分布式方式分析数据。实现第二个目标的方法依赖于新的、实用的 联合数据分析技术。例如，在垂直分区数据的情况下，与 相同的SHS参与者可以存储在不同的位置，例如基因组数据和表型数据 目前分别存储在SHS遗传学中心和SHS协调中心。第三个目标是 了解SHS参与者的数据共享期望和偏好，以便为实施 数据共享模型。这一目标将通过定性和定量的方法来实现， 包括使用对社会科学及人文科学参与者的个别访谈和调查。

PROJECT SUMMARY The Strong Heart Study (SHS) is a multi-center, longitudinal resource designed to better understand cardiovascular disease in American Indians, identify significant risk factors, promote new research and deliver better health care. To achieve these goals, SHS data should be accessible to interested and qualified researchers, while no harm is done to the study participants who contribute their data. Thus private information in the data and the identity of the participants should be protected, and SHS tribal sovereignty and agreements that include tribal review and approval of all SHS data use requests should be respected. Our study aims to address these issues using advanced technologies and scientific computing toolkits to enable shared, but protected, data access, as well as to understand the data sharing preferences of SHS participants. The first aim is to develop an innovative, secure data-centric service to protect computation on SHS data according to governance practices that are acceptable to participating SHS tribes, SHS investigators, and the NIH. Specifically, we will build a system for secure analysis on protected data through a virtual private network, in which only approved operations and outputs are permitted. The proposed framework will allow researchers to easily and securely perform specific statistical analysis on SHS data and meta-analyses. The second aim is to develop novel federated computing models to support the SHS Coordinating Center and Genetics Center to analyze data in a distributed manner. The methods for achieving the second aim rely on new, practical federated data analysis technology. For example, in the case of vertically partitioned data, different data from the same SHS participants may be stored at different sites, such as genomic data and phenotype data that are currently stored at the SHS Genetics Center and the SHS Coordinating Center, respectively. The third aim is to understand the data sharing expectations and preferences of SHS participants to inform the implementation of the data sharing models. This aim will be carried out through qualitative and quantitative methods, which include the use of individual interviews and surveys of SHS participants.