Exploring New Methods for Protecting and Distributing Confidential Research Data

探索保护和分发机密研究数据的新方法

• 批准号: 7826181
• 负责人: Felicia B. LeClere
• 金额: $ 49.75万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-30 至 2011-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7826181
• 关键词: Address; Adolescent; Agreement; Architecture; Archives; Area; Biological Markers; Collection; Communities; Computer Assisted; Confidentiality; Contracts; Custom; Data; Data Analyses; Data Collection; Data Files; Data Protection; Data Security; Data Storage and Retrieval; Disclosure; Education; Electronics; Environment; Family; Funding; Health; Healthcare; Home environment; Improve Access; Income; Individual; Information Systems; Information Technology; Institution; Legal; Longitudinal Studies; Los Angeles; Methods; Michigan; Monitor; Morbidity - disease rate; Neighborhoods; Performance; Physical environment; Process; Public Health; Recruitment Activity; Research; Research Ethics Committees; Research Infrastructure; Respondent; Retirement; Risk; Secure; Security; Security Measures; Site; Social Sciences; Surveys; System; Testing; Travel; United States National Institutes of Health; Universities; data sharing; design; distributed data; feeding; flexibility; improved; meetings; mortality; performance tests; programs; prototype; social; statistics; usability; virtual; web interface

DESCRIPTION (provided by applicant): This application addresses broad Challenge Area (10) Information Technology for Processing Health Care Data and specific Challenge Topic, 10-HD-102: Data Archiving and Dissemination. The sharp increase in the sophistication of social science data systems that accompanied computer- assisted data collection methods created a concomitant increase in the risk of disclosing individual respondent's identities when the data are shared more broadly. Public use data files, which substantially reduce the risk of disclosure through statistical and technical methods often also reduce the analytic utility of these data. Data producers have increasingly chosen to retain the original analytic potential of the data by releasing the files under a modified data use agreement or legal contract with analysts. Large data collection programs, both inside and outside the Federal Statistical System, increasingly issue a substantial number of these contracts annually. The contracts often place a large burden on the end user to provision and secure computing platforms that are designed to protect the electronic security of the data files. Different data systems also will often require separate machinery for each data use contract. This ad hoc system for securing and disseminating confidential data has limited both the availability and the security of the data. In this project, the Inter University Consortium for Political and Social Research and partners at the Rand Corporation and the Survey Research Center at the University of Michigan will build and test a data storage and dissemination system for confidential data, which obviates the need for users to build and secure their own computing environments. Recent advances in public utility (or "cloud") computing now makes it feasible to provision powerful, secure data analysis platforms on-demand. We will leverage these advances to build a system which collects "system configuration" information from analysts using a simple web interface, and then produces a custom computing environment for each confidential data contract holder. Each custom system will secure the data storage and usage environment in accordance with the confidentiality requirements of each data file. When the analysis has been completed, this custom system will be fed into a "virtual shredder" before final disposal. This prototype data dissemination system will be tested for (1) system functionality (i.e., does it remove the usual barriers to data access?); (2) storage and computing security (i.e., does it keep the data secure?); and (3) usability (i.e., is the entire system easier to use?). Contract holders of two major data systems (the Panel Study of Income Dynamics and the Los Angeles Family and Neighborhood Study) will be recruited to assess both the user interface and the analytic flexibility of the new customized computing environments. The project is designed to improve access to important public health data by lowering barriers to sophisticated research collections. Data systems that include information on morbidity, mortality, and biomarkers will be more accessible to the research community. The computing architecture described here will also improve data security and, thus, encourage more widespread sharing of research data.

描述（由申请人提供）： 此应用程序解决了广泛的挑战领域（10）信息技术处理医疗保健数据和特定的挑战主题，10-HD-102：数据存储和传播。随着计算机辅助数据收集方法的出现，社会科学数据系统的复杂性急剧增加，在更广泛地分享数据时，暴露个别答复者身份的风险也随之增加。公共使用数据文件通过统计和技术方法大大减少了披露的风险，但往往也减少了这些数据的分析效用。数据生产者越来越多地选择保留数据的原始分析潜力，根据修改后的数据使用协议或与分析师签订的法律的合同发布文件。联邦统计系统内外的大型数据收集方案每年越来越多地签发大量此类合同。这些合同往往给最终用户带来很大的负担，使其无法提供和保护旨在保护数据文件电子安全的计算平台。不同的数据系统也往往需要为每一个数据使用合同单独的机制。这种保护和传播机密数据的临时制度限制了数据的提供和安全。在这个项目中，大学间政治和社会研究联合会与兰德公司和密歇根大学调查研究中心的合作伙伴将建立和测试一个机密数据的数据存储和传播系统，这消除了用户建立和保护自己的计算环境的需要。公共事业（或“云”）计算的最新进展现在使得按需提供强大，安全的数据分析平台变得可行。我们将利用这些进步来建立一个系统，该系统使用简单的网络界面从分析师那里收集“系统配置”信息，然后为每个机密数据合同保持器生成一个自定义计算环境。每个定制系统将根据每个数据文件的保密要求保护数据存储和使用环境。当分析完成后，这个定制的系统将被送入一个“虚拟粉碎机”，然后进行最终处置。将对这一原型数据传播系统进行以下方面的测试：（1）系统功能（即，它是否消除了数据访问的通常障碍？）; (2)存储和计算安全性（即，它能保证数据的安全吗？）;以及（3）可用性（即，整个系统是否更容易使用？）。将招募两个主要数据系统（收入动态小组研究和洛杉矶家庭和邻里研究）的合同持有人，以评估新的定制计算环境的用户界面和分析灵活性。该项目旨在通过降低复杂研究收集的障碍来改善重要公共卫生数据的获取。包括发病率、死亡率和生物标志物信息的数据系统将更容易为研究界所利用。这里描述的计算架构还将提高数据安全性，从而鼓励更广泛地共享研究数据。