(t,k)‐Hypergraph anonymization: an approach for secure data publishing
Atefeh Asayesh, Mohammad Ali Hadavi, Rasool Jalili · Security and Communication Networks · 2014
Abstract Privacy preservation is an important issue in data publishing. Existing approaches on privacy‐preserving data publishing rely on tabular anonymization techniques such ask‐anonymity, which do not provide appropriate results for aggregate queries. The solutions based on graph anonymization have also been proposed for relational data to hide only bipartite relations. In this paper, we propose an approach for anonymizing multirelation constraints (ternary or more) with (t,k) hypergraph anonymization in data publishing. To this end, we model constraints as undirected hypergraphs and formally cluster attribute relations as hyperedge with thet‐means‐clustering algorithm. In addition, anonymization is carried out with ak‐anonymity method in every cluster for which the parameterkcan vary in each cluster, to attain more flexibility and less information loss with respect to utility. Our experiments demonstrate that this approach offers a great trade‐off between privacy and utility. Copyright © 2014 John Wiley & Sons, Ltd.