Microdata protection method through microaggregation: a median-based approach

Abstract

Microaggregation for statistical disclosure control (SDC) is a family of methods to protect microdata from individual identification. SDC seeks to protect microdata in such a way that they can be published and mined without providing any private information that can be linked to specific individuals. The aim of SDC is to modify the original microdata in such a way that the modified data and the original data are similar. Microaggregation works by partitioning the microdata into groups, also called clusters of at least k records, and then replacing the records in each group with the centroid of the group. In this work we introduce a new microaggregation method where the centroid is considered as median. The new method guarantees that the microaggregated data and the original data are similar by using statistical tests. Another contribution of this work is that we propose a distance metric, called absolute deviation from median (ADM), to evaluate the amount of mutual information among records in microdata. We showed that ADM is always less than the most commonly used measure of distortion called sum of squares of errors (SSE) for any dataset. Thus, ADM causes the least information loss and can be used as a measure of information loss for a microaggregated microdata set.