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）： 该应用程序解决了广泛的挑战领域 (10) 用于处理医疗保健数据的信息技术和特定挑战主题 10-HD-102：数据存档和传播。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，当数据更广泛地共享时，披露个人受访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降低了披露风险，但通常也会降低这些数据的分析效用。数据生产者越来越多地选择通过根据修改后的数据使用协议或与分析师的法律合同发布文件来保留数据的原始分析潜力。联邦统计系统内部和外部的大型数据收集项目每年都会越来越多地发布大量此类合同。这些合同通常会给最终用户带来沉重的负担，因为他们需要提供和保护旨在保护数据文件的电子安全的计算平台。不同的数据系统通常还需要针对每个数据使用合同使用单独的机器。这种用于保护和传播机密数据的临时系统限制了数据的可用性和安全性。在这个项目中，大学间政治和社会研究联盟以及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机密数据的数据存储和传播系统，这使得用户无需构建并保护自己的计算环境。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成为可能。我们将利用这些进步来构建一个系统，该系统使用简单的 Web 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持有者生成自定义计算环境。每个定制系统都会根据每个数据文件的保密要求来保护数据存储和使用环境。分析完成后，该定制系统将在最终处置之前被送入“虚拟粉碎机”。该原型数据分发系统将针对 (1) 系统功能进行测试（即，它是否消除了数据访问的常见障碍？）； (2) 存储和计算安全（即是否保证数据安全？）； (3)可用性（即整个系统是否更易于使用？）。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）的合同持有者来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。研究界将更容易获得包括发病率、死亡率和生物标志物信息的数据系统。这里描述的计算架构还将提高数据安全性，从而鼓励更广泛的研究数据共享。