(t, k)‐Hypergraph anonymization: an approach for secure data publishing. Issue 7 (25th September 2014)

• Record Type: Journal Article
• Authors: Asayesh, Atefeh; Hadavi, Mohammad Ali; Jalili, Rasool
• Is Part Of: Security and communication networks. Volume 8:Issue 7(2015)
• Page Start: 1306
• Page End: 1317
• Publication Date: 2014-09-25
• Subjects: data publishing -- privacy -- anonymization -- hypergraph -- clustering; Computer networks -- Security measures -- Periodicals; Computer security -- Periodicals; Cryptography -- Periodicals; 005.805
• DOI: 10.1002/sec.1084
• Languages: English
• ISSNs: 1939-0114

Abstract

Abstract: 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, anonymization is carried out with a k ‐anonymity method in every cluster for which the parameter k can 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. Abstract : In this paper, we propose an approach for anonymizing multirelation constraints (ternary or more) with (t, k) hypergraph anonymization in data publishing. In this model, we use t‐means‐clustering algorithm. This algorithm classifies the objects based on attributes/features into t groups where t is a positive integer number. The grouping is carried out by minimizing the sum of squares of distances between data and the corresponding cluster centroid. In addition, anonymization is carried out with a k‐anonymity method in every cluster.