Exploring New Methods for Protecting and Distributing Confidential Research Data

描述(由申请人提供)： 该应用程序涉及广泛的挑战领域(10)用于处理卫生保健数据的信息技术和具体的挑战主题，10-HD-102：数据存档和传播。伴随着计算机辅助数据收集方法而来的社会科学数据系统的复杂性急剧增加，从而增加了在更广泛地共享数据时披露被调查者个人身份的风险。公共使用的数据文件通过统计和技术方法大大降低了披露风险，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，以保留数据的原始分析潜力。无论是联邦统计系统内部还是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。这些合同往往给最终用户带来很大的负担，以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为每个数据使用合同使用不同的机器。这种保护和传播机密数据的特别系统限制了数据的可获得性和安全性。在该项目中，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，用户无需建立和保护自己的计算环境。公用事业(或“云”)计算的最新进展现在使按需提供强大、安全的数据分析平台成为可能。我们将利用这些进步来建立一个系统，该系统使用一个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持有人产生一个定制的计算环境。每个定制系统将根据每个数据文件的保密要求确保数据存储和使用环境的安全。当分析完成后，在最终处置之前，这个定制系统将被送入“虚拟碎纸机”。这个原型数据传播系统将进行以下测试：(1)系统功能(即，它是否消除了数据访问的常见障碍？)；(2)存储和计算安全性(即，它是否确保数据安全？)；(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)的合同者，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门槛，改善重要公共卫生数据的获取。包括发病率、死亡率和生物标记物信息的数据系统将更容易为研究界所访问。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。

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.