Probabilistic record linkage for disclosure risk assessment

Abstract

Disclosure limitation methods for protecting the confidentiality of respondents in survey microdata often use perturbative techniques which introduce measurement error into the categorical identifying variables. In addition, the data itself will often have measurement errors commonly arising from survey processes. There is a need for valid and practical ways to assess the protection against the risk of identification for survey microdata with measurement errors. A common disclosure risk scenario is when an intruder seeks to match the microdata with an external file. We will examine probabilistic record linkage as a means of assessing disclosure risk and relate it to disclosure risk measures under the probabilistic framework of the Poisson log-linear models.