A New Anonymization Model for Privacy Preserving Data Publishing: CANON

Abstract

Data privacy is a challenging trade-off problem between privacy preserving and data utility. Anonymization is a fundamental approach for privacy preserving and also a hard trade-off problem. It enables to hide the identities of data subjects or record owners and requires to be developed near-optimal solutions. In this paper, a new multidimensional anonymization model (CANON) that employs vantage-point tree (VPtree) and multidimensional generalization for greedy partitioning and anonymization, respectively, is proposed and introduced successfully for the first time. The main concept of CANON is inspired from Mondrian, which is an anonymization model for privacy preserving data publishing. Experimental results have shown that CANON takes data distribution into consideration and creates equivalence classes including closer data points than Mondrian. As a result, CANON provides better data utility than Mondrian in terms of GCP metric and it is a promising anonymization model for future works.