Sharif Digital Repository

Privacy preservation is an important issue in data publishing. Existing approaches on privacy-preserving data publishing rely on tabular anonymization techniques such as k-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 the t-means-clustering algorithm. In addition,...