Title: An efficient privacy-preservation algorithm for incremental data publishing

Authors: Torsak Soontornphand; Mizuho Iwaihara; Juggapong Natwichai

Addresses: Data Engineering Laboratory, Computer Engineering Department, Faculty of Engineering, Chiang Mai University, Muang District, Chiang Mai, Thailand ' Graduate School of Information, Production and Systems, Waseda University, Kitakyushu, Fukuoka, Japan ' Data Engineering Laboratory, Computer Engineering Department, Faculty of Engineering and Data Analytics and Knowledge Synthesis for Healthcare, Chiang Mai University, Muang District, Chiang Mai, Thailand

Abstract: Data can be continuously collected and grown all the time. Privacy protection designed for static data might not be able to cope with this situation effectively. In this paper, we present an efficient privacy preservation approach based on (k, l)-anonymity for incremental data publishing. We first illustrate the three privacy attacks, i.e., similarity, difference and joint attacks. Then, the three characteristics of incremental data publishing are analysed and exploited to efficiently detect privacy violations. With the studied characteristics, the similarity and join attack detection can be skipped for stable releases. In addition, only a subtype of the similarity attack and the latest previously released data set need to be detected. From experimental results, the proposed method is highly efficient, with an average execution time eleven times less than a compared static algorithm. In addition, the proposed method can also maintain better data quality than the compared methods at every setting.

Keywords: privacy preservation; incremental data publishing; privacy attack; full-domain generalisation.

DOI: 10.1504/IJGUC.2023.135303

International Journal of Grid and Utility Computing, 2023 Vol.14 No.6, pp.562 - 582

Received: 09 Jun 2021
Accepted: 01 Sep 2021
Published online: 05 Dec 2023 *

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