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首页> 外文期刊>Journal of applied statistics >Post-randomization for controlling identification risk in releasing microdata from general surveys
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Post-randomization for controlling identification risk in releasing microdata from general surveys

机译:从一般调查中控制释放微基数据的识别风险后的随机后化

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摘要

Before releasing survey data, statistical agencies usually perturb the original data to keep each survey unit's information confidential. One significant concern in releasing survey microdata is identity disclosure, which occurs when an intruder correctly identifies the records of a survey unit by matching the values of some key (or pseudo-identifying) variables. We examine a recently developed post-randomization method for a strict control of identification risks in releasing survey microdata. While that procedure well preserves the observed frequencies and hence statistical estimates in case of simple random sampling, we show that in general surveys, it may induce considerable bias in commonly used survey-weighted estimators. We propose a modified procedure that better preserves weighted estimates. The procedure is illustrated and empirically assessed with an application to a publicly available US Census Bureau data set.
机译:在释放调查数据之前,统计机构通常会使原始数据扰乱原始数据,以保留每个调查单位的信息机密信息。 释放调查微数据的一个重要关注是身份披露,当入侵者通过匹配某个键(或伪识别)变量的值正确地识别调查单元的记录时发生。 我们研究了最近开发的随机后化方法,以严格控制释放测量微数据的识别风险。 虽然该程序很好地保留了观察到的频率,因此在简单的随机抽样情况下统计估计,我们表明,在一般调查中,它可能会引起常用的测量加权估计器中的相当大的偏差。 我们提出了一种更好地保留加权估计的修改过程。 该程序被申请和经验评估,以公开的美国人口普查局数据集。

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