Post-randomization for controlling identification risk in releasing microdata from general surveys

Cheng Zhang and Tapan K. Nayak

Journal of Applied Statistics, 2021, vol. 48, issue 3, 455-470

Abstract: 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.

Date: 2021
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DOI: 10.1080/02664763.2020.1732310

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