Synthetic Data User Testing and Dissemination

综合数据用户测试和传播

• 批准号: 1042181
• 负责人: Lars Vilhuber
• 金额: $ 19.37万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-15 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1042181&HistoricalAwards=false
• 关键词: Synthetic; Data; User; Testing; Dissemination

Researchers throughout the social, behavioral, economic, and health sciences use data to test hypotheses about a wide range of individual and social behaviors, decisions, and outcomes. Government statistical agencies regularly collect data that are extremely valuable for this purpose. However, these data are not made directly available to the research community because the data providers' (responents') identity is part of the data itself. Therefore statistical agencies and the scientific community have been developing methods to make analytically valid and highly detailed data available to researchers while simultaneously protecting individual privacy.A particularly valuable and sensitive kind of data is linked administrative data such as the Longitudinal Employer-Household Data (LEHD), the Longitudinal Business Database (LBD) and surveys with linked administrative data (SIPP). These datasets have been constructed with support from statistical agencies and the NSF. The highly detailed nature of these data make them particularly sensitive, and access to the micro-data remains restriced. One approach for balancing the tension between confientiality protection and access is the generation of synthetic data. The process for generating such data begins by estimating a posterior predictive distribution (PPD) of the to-be-released data given the confidential micro-data. The next step is to draw samples from the PPD to produce the released micro-data. The quality of inferences based on a wide variety of models applied to synthetic and actual data has been indaquately assessed to date because only a limited number of users have had access to both data sources. This kind of assessment needs to be integrated within a quality-feedback loop in order to improve synthetic data and increase the use of the data by the research community. This award facilitiates such a feedback loop for synthetic versions of two datasetss: the Census Bureau's Survey of Income and Program Participation and the Longitudinal Business Database. The goal is to broaden access to the data, enhance the feedback loop, and provide flexible and secure access to these synthetic data early releases.A variety of social scientists from a range of disciplines will be able to use this data access method and will provide detailed input that will guide future improvements in data quality.

整个社会、行为、经济和健康科学的研究人员使用数据来测试关于个人和社会行为、决策和结果的各种假设。政府统计机构定期收集对这一目的极其有价值的数据。然而，这些数据不能直接提供给研究界，因为数据提供者(答复者)的身份是数据本身的一部分。因此，统计机构和科学界一直在开发方法，以便向研究人员提供分析上有效和高度详细的数据，同时保护个人隐私。一种特别有价值和敏感的数据是链接的行政数据，如纵向雇主-家庭数据(LHD)、纵向商业数据库(LBD)和带有链接的行政数据的调查(SIPP)。这些数据集是在统计机构和国家科学基金会的支持下构建的。这些数据的高度详细性质使它们特别敏感，对微观数据的获取仍然受到限制。平衡保密性保护和访问之间紧张关系的一种方法是生成合成数据。在给定机密微数据的情况下，通过估计要发布的数据的后验预测分布(PPD)来开始生成这种数据的过程。下一步是从PPD中抽取样本，以产生发布的微观数据。迄今为止，对应用于合成数据和实际数据的各种模型所作的推论的质量进行了令人信服的评估，因为只有有限数量的用户能够访问这两个数据来源。这类评估需要纳入质量反馈循环，以便改进综合数据并增加研究界对数据的使用。这一奖项为两个数据集的合成版本提供了这样的反馈循环：人口普查局的收入和计划参与调查以及纵向商业数据库。目标是扩大对数据的访问，增强反馈循环，并提供对这些早期发布的合成数据的灵活和安全的访问。来自不同学科的各种社会科学家将能够使用这种数据访问方法，并将提供详细的输入，以指导未来数据质量的改进。