Precision threshold and noise: An alternative framework of sensitivity measures

Abstract

At many national statistical organizations, linear sensitivity measures such as the prior-posterior and dominance rules provide the basis for assessing statistical disclosure risk in tabular magnitude data. However, these measures are not always well-suited for issues present in survey data such as negative values, respondent waivers and sampling weights. In order to address this gap, this paper introduces the Precision Threshold and Noise framework, defining a new class of sensitivity measures. These measures expand upon existing theory by relaxing certain restrictions, providing a powerful, flexible and functional tool for national statistical organizations in the assessment of disclosure risk.