Finding a Needle in a Haystack: The Theoretical and Empirical Foundations of Assessing Disclosure Risk for Contextualized Microdata

Abstract

Contextualized microdata are one way to safely release geographic data without identifying the location of survey respondents. This study informs the design of such datafiles with its needle-in-haystack approach to disclosure and its discussion of associated methodological concerns. Drawing a sample of counties, tracts, and blockgroups, I illustrate how the reidentification of individuals is shaped by aggregating geographies into look-alike sets. I detail the complexity of reidentification patterns by assessing the likelihood that young adult white and black males would be pinpointed within reconstituted haystacks given: (1) the size of the total population of aggregated contexts; (2) the amount of error in population counts; and (3) differential search costs stemming from spatially dispersed contexts.