Achieving k-anonymity via a density-based clustering method

Abstract

The key idea of our k-anonymity is to cluster the personal data based on the density which is measured by the k-Nearest-Neighbor (KNN) distance. We add a constraint that each cluster contains at least k records which is not the same as the traditional clustering methods, and provide an algorithm to come up with such a clustering. We also develop more appropriate metrics to measure the distance and information loss, which is suitable in both numeric and categorical attributes. Experiment results show that our algorithm causes significantly less information loss than previous proposed clustering algorithms. © Springer-Verlag Berlin Heidelberg 2007.