Global disclosure risk for microdata with continuous attributes

Abstract

In this paper, we introduce three global disclosure risk measures (minimal, maximal and weighted) for microdata with continuous attributes. We classify the attributes of a given set of microdata in two different ways: based on its potential identification utility and based on the order relation that exists in its domain of value. We define inversion factors that allow data users to quantify the magnitude of masking modification incurred for values of a key attribute. We create vicinity sets from microdata for each record based on distance functions or interval vicinity for each key attribute value. The disclosure risk measures are based on inversion factors and the vicinity sets' cardinality computed for both initial and masked microdata. © 2006 Springer Science+Business Media, Inc.