NCRN-MN: Cornell Census-NSF Research Node: Integrated Research Support, Training and Data Documentation

NCRN-MN：康奈尔大学人口普查-NSF 研究节点：综合研究支持、培训和数据文档

• 批准号: 1131848
• 负责人: Lars Vilhuber
• 金额: $ 299.96万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-10-01 至 2018-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1131848&HistoricalAwards=false
• 关键词: NCRN; MN; Cornell; Census; NSF

The era of public-use micro-datasets as a cornerstone of empirical research in the social sciences is coming to an end. While it still is feasible to create such data without breaching confidentiality, scholars are pursuing research programs that mandate inherently identifiable data, such as geospatial relations, exact genome data, networks of all sorts, and linked administrative records. These researchers acquire authorized restricted access to the confidential identifiable data and perform their analyses in secure environments. The researcher is allowed to publish results that have been filtered through a statistical disclosure limitation protocol. Scientific scrutiny is hampered because the researcher cannot effectively implement a data-management plan that permits sharing these restricted-access data with other scholars. The data-custody problem is impeding the "acquire, archive, and curate" model that dominated social science data preservation in the era of public-use micro-data. This project will bridge the transition to restricted-access data and offer the scholar, the scientific community, and the custodial agency a feasible path to long-term data preservation. The Comprehensive Census Bureau Metadata Repository (CCBMR) will be a Data Documentation Initiative-based curation system designed and implemented in a manner that permits synchronization between the public and confidential versions of the repository. The scholarly community will use the CCBMR as it would use a conventional metadata repository, deprived only of the values of certain confidential information, but not their metadata. The authorized user, working on the secure Census Bureau network, will use the CCBMR with full information in authorized domains. There is no duplication of effort, and the project will implement fully automatic disclosure avoidance review of the metadata where feasible. The preservation function operates indefinitely on the original scientific inputs as long as the researchers cooperate and the agency continues to fund the preservation component of the CCBMR. Doctoral students will be taught how to develop research programs using restricted-access Census Bureau data and the repository tools developed in this project in combination with previously developed tools. The same tools will be used to develop computational statistics algorithms based on boosting to improve the integration, editing, and imputation models that assemble the micro-data used for the Census Bureau's longitudinally linked employer-employee database.Because the Confidential Information Protection and Statistical Efficiency Act of 2002 formalized the obligation of every statistical agency in the United States to take long-term custody of the confidential micro-data used for its work, all federal statistical agencies face the same problem as the Census Bureau. The CCBMR, the education based on this repository, and the collaborative computational statistics model all can be generalized to meet the restricted-access research requirements of other statistical agencies. These tools allow statistical agencies to harness the efforts of researchers who want to understand the structure and complexity of the confidential data they intend to analyze in order to propose and implement reproducible scientific results. Future generations of scientists will be able to build on those efforts because the long-term data preservation in the CCBMR will operate on the original scientific inputs, not inputs that have been subjected to statistical disclosure limitation prior to entering the repository. This curation will result in a viable system for enforcing data management plans on projects, ensuring that results can be tested and replicated by future scientists. This activity is supported by the NSF-Census Research Network funding opportunity.

作为社会科学实证研究基石的公共使用微数据集的时代即将结束。 虽然在不违反保密性的情况下创建此类数据仍然是可行的，但学者们正在推行强制要求固有可识别数据的研究计划，例如地理空间关系、精确的基因组数据、各种网络和关联的行政记录。 这些研究人员获得对机密可识别数据的授权限制访问权限，并在安全环境中进行分析。 研究人员被允许发表通过统计披露限制协议过滤的结果。 科学审查受到阻碍，因为研究人员无法有效地实施数据管理计划，允许与其他学者共享这些限制访问的数据。 数据保管问题正在阻碍“获取、存档和管理”模式，这种模式在公共使用微观数据时代主导着社会科学数据的保存。 该项目将连接向限制访问数据的过渡，并为学者、科学界和保管机构提供长期数据保存的可行途径。 人口普查局元数据综合储存库将是一个基于数据文件倡议的管理系统，其设计和实施方式允许储存库的公开版本和保密版本同步。 学术界将使用CCBMR，因为它将使用传统的元数据存储库，只剥夺了某些机密信息的价值，但不剥夺其元数据。 在安全的人口普查局网络上工作的授权用户将使用CCBMR，其中包含授权域中的全部信息。 没有重复工作，该项目将在可行时对元数据进行完全自动的避免披露审查。 只要研究人员合作，并且该机构继续为CCBMR的保存部分提供资金，保存功能就可以无限期地在原始科学投入上运作。 博士生将被教导如何开发研究计划，使用限制访问人口普查局的数据和存储库工具在这个项目中开发的结合以前开发的工具。 相同的工具将用于开发基于提升的计算统计算法，以改善集成，编辑，由于2002年的《机密信息保护和统计效率法》正式规定，美国每个统计机构都有义务在2002年之前，由于长期保管用于其工作的机密微观数据，所有联邦统计机构都面临与人口普查局相同的问题。 CCBMR、基于该知识库的教育以及协作计算统计模型都可以推广，以满足其他统计机构的限制访问研究要求。 这些工具使统计机构能够利用研究人员的努力，这些研究人员希望了解他们打算分析的机密数据的结构和复杂性，以便提出和实施可重复的科学结果。 未来几代科学家将能够在这些努力的基础上再接再厉，因为CCBMR中的长期数据保存将基于原始科学投入，而不是在进入存储库之前受到统计披露限制的投入。 这种管理将产生一个可行的系统，用于在项目中执行数据管理计划，确保未来的科学家可以测试和复制结果。 这项活动得到了NSF-人口普查研究网络资助机会的支持。

项目成果

Why the Economics Profession Must Actively Participate in the Privacy Protection Debate

为什么经济学界必须积极参与隐私保护争论

• 发表时间: 2019
• 期刊: American Economic Review: Papers and Proceedings
• 作者: Abowd, John M.;Schmutte, Ian;Sexton, William;Vilhuber, Lars
• 通讯作者: Vilhuber, Lars