Addressing Disclosure Risk of Contextualized Microdata in Survey Design

解决调查设计中情境化微观数据的披露风险

• 批准号: 9204318
• 负责人: MICHAEL R. ELLIOTT
• 金额: $ 49.39万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-01-16 至 2018-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9204318
• 关键词: Address; Affect; Agreement; Asthma; Behavior; Cardiovascular Diseases; Categories; Characteristics; Collection; Confidential Information; County; Cuban; Data; Data Collection; Disclosure; Disease Progression; Dominican; Environment; Equilibrium; Ethnic Origin; Evaluation; Geographic Locations; Geography; Goals; Guidelines; Health; Hispanics; Individual; Investments; Knowledge; Literature; Lung diseases; Measures; Methods; Mexican; Neighborhoods; Obesity; Outcome; Participant; Persons; Phase; Policies; Prevalence; Puerto Rican; Research; Respondent; Rights; Risk; Risk Estimate; Sampling; Science; Smoking; Social Sciences; Study Subject; Surveys; Techniques; cost; database design; design; health disparity; predictive modeling; residence; simulation

DESCRIPTION (provided by applicant): This project seeks to increase the availability of detailed research data about a person's neighborhood and individual characteristics, behaviors, and health outcomes, information which is crucial for research on critical national issues, such as health disparities. However, a delicate balance must be struck between providing easy access to these data and protecting the anonymity of study participants. Responding to the rising demand for contextualized microdata, large national surveys typically collect meticulous information about their subjects' personal and geographic attributes. When data are prepared for public-use files, however, much of this important detail is either suppressed or coarsened to protect the anonymity of respondents. These limitations reduce opportunities for important scientific research and impose costly burdens on producers and distributors who must implement restrictive data use agreements. Little is known about how the ability to protect a respondent's identity (i.e., disclosure risk) is affected by releasing microdata files that contain the contextual attributes of counties, tracts, blockgroups, and 1/2-mile geographic areas surrounding each subject. Considering factors that are determined at the outset of a study, it is not known how disclosure risk of contextualized microdata is affected by varying levels of sensitive information, or different sampling designs and analytical purposes. Turning to factors that are usually addressed after data collection when research files are prepared for dissemination, it is not known to what extent that disclosure risk and the scientific value of data is affected by the selection of different variables for release or application of various statistical techniques to limit disclosure. With a priori knowledge of these determinants, data producers will be able to anticipate how many and which respondents are at risk of disclosure, and adapt their data collection methods to protect them. Such adjustments will preserve and enhance the utility of the data for broad dissemination. Also, factors that affect data collection efficiencies can then be measured, allowing for the estimation of survey costs associated with modifying sampling designs to meet disclosure goals. Hence this project seeks to incorporate disclosure risk into the conceptual and empirical frameworks used in the evaluation of survey designs. In so doing, we first develop and validate models that predict the composition of survey data under different sampling designs. Next we develop measures and methods used in the assessments of disclosure risk, analytical utility, and disclosure survey costs that are best suited for evaluating sampling and database designs. Lastly we conduct simulations to gather estimates of risk, utility, and cost for studies with a wide range of sampling and database design characteristics.

描述(由申请者提供)：这个项目寻求增加关于一个人的社区和个人特征、行为和健康结果的详细研究数据，这些信息对于研究关键的国家问题，如健康差距，是至关重要的。然而，必须在提供这些数据的方便访问和保护研究参与者的匿名性之间取得微妙的平衡。为了应对对背景微观数据日益增长的需求，大型全国性调查通常会收集关于受试者个人和地理属性的详细信息。然而，当为公共用途的文件准备数据时，这些重要细节中的大部分要么被隐藏，要么被粗略处理，以保护受访者的匿名性。这些限制减少了重要科学研究的机会，并给必须执行限制性数据使用协议的生产者和分销商带来了代价高昂的负担。关于发布微数据文件如何影响保护受访者身份的能力(即披露风险)，这些微数据文件包含每个对象周围的县、区域、区块组和1/2英里的地理区域的上下文属性，人们知之甚少。考虑到在研究开始时确定的因素，不知道不同程度的敏感信息或不同的抽样设计和分析目的如何影响背景微数据的披露风险。关于通常在收集数据后准备分发研究文件时处理的因素，目前尚不清楚选择不同的变量发布或应用各种统计技术限制披露对披露风险和数据的科学价值有多大影响。有了这些决定因素的先验知识，数据制作者将能够预测有多少受访者以及哪些受访者面临披露风险，并调整他们的数据收集方法以保护他们。这样的调整将保留和加强数据的用途，以便广泛传播。此外，还可以衡量影响数据收集效率的因素，以便估计与修改抽样设计以满足披露目标相关的调查成本。因此，该项目力求将披露风险纳入评价调查设计时使用的概念和经验框架。在这样做的过程中，我们首先开发和验证预测不同抽样设计下的调查数据组成的模型。接下来，我们开发用于评估披露风险、分析效用和披露调查成本的措施和方法，这些措施和方法最适合于评估抽样和数据库设计。最后，我们进行模拟，以收集风险、效用和成本的估计，用于具有广泛的抽样和数据库设计特征的研究。