Estimation of Disclosure Risk and Statistical Methods for Disclosure Limitations

最近，研究人员和政策制定者对微观数据的需求激增，特别是在数据收集是用公共资金支付的情况下，这加剧了人们对保密保护的担忧。 对答复保密是数据收集机构对研究参与者作出的一项严肃承诺。任何将数据传播给研究人员和政策制定者的机构都应该做出类似的承诺。随着对微观数据需求的增加，一些商业数据库的名称和地址以及人口信息等识别信息也变得可供使用。这些数据库使人担心，入侵者可能会将数据收集机构发布的供公众使用的匿名调查数据与商业数据库联系起来，以确定调查的一个或多个答复者的身份。这项研究提案有三个主要目标：(1)评估 使用涵盖各种主题的四个试验台全国概率调查的数据进行披露。风险将使用两大类入侵者模型来解决。第一类，即入侵者亲自认识的个人，被入侵者知道在调查中；以及第二类，即有权进入带有姓名和地址的外部数据库的入侵者试图确定调查中受访者的身份，从而获得机密信息；(2)开发和评估避免披露的新方法；(3)制定战略，通过汇总变量来替换被认为增加披露风险的公共使用数据集中的变量，使用户能够在不知道这些变量的实际值的情况下对这些变量进行调整或控制。

The recent explosion in demands for microdata from researchers and policy makers, especially when the data collection is paid for with public funds, has increased concerns about confidentiality protection. Confidentiality of responses is a serious commitment made by data-collecting agencies to participants in the study. A similar commitment is expected of any agency that disseminates the data to researchers and policy makers. Along with the increased demands for microdata, a number of commercial databases with the identifying information such as names and addresses and demographic information have also become accessible. These databases raise the concern that an intruder can potentially link the anonymous survey data released by the data collection agencies for public use with the commercial databases to identify one or more respondents to the survey. This research proposal has three primary objectives: (1) To assess the risk of disclosure using data from four test-bed national probability surveys covering a wide variety of topics. The risk will be addressed using two broad classes of intruder models. Type I, where an individual, personally known to the intruder, is known by the intruder to be in the survey; and Type II, where an intruder with access to an external database with names and addresses is seeking to identify respondents in the survey and hence gain access to confidential information; (2) To develop and evaluate new methods to avoid disclosure, and (3) To develop strategies for replacing variables in public-use data sets deemed to increase the risk of disclosure by summary variables that allow users to adjust or control for these variables without knowing their actual values.