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Enhanced P-Sensitive K-Anonymity Models for Privacy Preserving Data Publishing

Xiaoxun Sun ^(a),(*), Hua Wang^(a), Jiuyong Li^(b), Traian Marius Truta^(c)

Transactions on Data Privacy 1:2 (2008) 53 - 66

Abstract, PDF

(a) Department of Mathematics & Computing; University of Southern Queensland; Queensland; Australia. e-mail: {sunx, wang}@usq.edu.au

(b) School of Computer and Information Science; University of South Australia; Adelaide; Australia. e-mail: jiuyong.li@unisa.edu.au

(c) Department of Computer Science; Northern Kentucky University; Highland Heights; KY; USA. e-mail: trutat1@nku.edu

Abstract

Publishing data for analysis from a micro data table containing sensitive attributes, while maintaining individual privacy, is a problem of increasing significance today. The k-anonymity model was proposed for privacy preserving data publication. While focusing on identity disclosure, k-anonymity model fails to protect attribute disclosure to some extent. Many efforts are made to enhance the k-anonymity model recently. In this paper, we propose two new privacy protection models called (p, α)-sensitive k-anonymity and (p⁺, α)-sensitive k-anonymity, respectively. Different from previous the p-sensitive k-anonymity model, these new introduced models allow us to release a lot more information without compromising privacy. Moreover, we prove that the (p, α)-sensitive and (p⁺, α)-sensitive k-anonymity problems are NP-hard. We also include testing and heuristic generating algorithms to generate desired micro data table. Experimental results show that our introduced model could significantly reduce the privacy breach.

^* Corresponding author.

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