Title: Utility-based anonymisation for dataset with multiple sensitive attributes

Authors: Lixia Wang; Qing Zhu

Addresses: Key Laboratory for Data Engineering and Knowledge Engineering MOE, School of Information, Renmin University of China, Beijing 100872, China ' Key Laboratory for Data Engineering and Knowledge Engineering MOE, School of Information, Renmin University of China, Beijing 100872, China

Abstract: Privacy-preserving data publication problem has attracted more and more attentions in recent years. A lot of related research works have been done towards dataset with single sensitive attribute. However, usually, original dataset contains more than one sensitive attribute. In this paper, we apply k-anonymity principle to solve the data publication problem for dataset with multiple sensitive attributes. We first cluster sensitive values based on a utility matrix. Then, we use a greedy strategy to partition tuples into equivalence classes. Our method can guarantee that the size of equivalence class is k except the last one, which reduces information loss. Also, we can guarantee the diversity of sensitive value in an equivalence class, which can protect privacy against the homogeneity attack. Experiments on a real dataset show that our method performs well on information loss, which indicates that we can guarantee data utility while protecting personal privacy.

Keywords: utility matrix; anonymisation; k-anonymity; multiple attributes; sensitive attributes; privacy preservation; privacy protection; data publication; greedy strategy; equivalence class; information loss; diversity.

DOI: 10.1504/IJHPCN.2016.10001327

International Journal of High Performance Computing and Networking, 2016 Vol.9 No.5/6, pp.401 - 408

Available online: 22 Nov 2016 *

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