Title: Local anatomy for personalised privacy protection

Authors: Boyu Li; Yanheng Liu; Minghai Wang; Geng Sun; Bin Li

Addresses: College of Computer Science and Technology, Jilin University, Changchun, China ' Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, China ' Big Data and Network Management Center, Jilin University, Changchun, China ' College of Computer Science and Technology, Jilin University, Changchun, China ' College of Mathematics, Jilin University, Changchun, China

Abstract: Anonymisation technique has been extensively studied and widely applied for privacy-preserving data publishing. However, most existing methods ignore personal anonymity requirements. In these approaches, the microdata consist of three categories of attribute: explicit-identifier, quasi-identifier and sensitive attribute. In fact, the data sensitivity should be determined by individuals. An attribute is semi-sensitive if it contains both QI and sensitive values. In this paper, we propose a novel anonymisation approach, called local anatomy, to address personalised privacy protection. Local anatomy partitions the tuples who consider the value as sensitive into buckets inside each attribute. We conduct some experiments to illustrate that local anatomy can protect all the sensitive values and preserve great information utility. Additionally, we also present the concept of intelligent anonymisation system as our direction of future work.

Keywords: data publishing; personalised privacy protection; semi-sensitive attribute.

DOI: 10.1504/IJICS.2021.116308

International Journal of Information and Computer Security, 2021 Vol.15 No.2/3, pp.254 - 271

Received: 17 Jun 2017
Accepted: 24 May 2018

Published online: 09 Jul 2021 *

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