Title: Privacy-aware trajectory data publishing: an optimal efficient generalisation algorithm

Authors: Nattapon Harnsamut; Juggapong Natwichai

Addresses: Data Engineering Laboratory, Computer Engineering Department, Faculty of Engineering, Chiang Mai University, Chiang Mai, Thailand ' Data Engineering Laboratory, Department of Computer Engineering, Faculty of Engineering and Centre of Data Analytics and Knowledge Synthesis for Healthcare, Chiang Mai University, Chiang Mai, Thailand

Abstract: With the increasing location-aware technologies that provide positioning services, it is easy to collect users' trajectory data. Such data could often contain sensitive information, i.e., private locations, private trajectory or path, and other sensitive attributes. In this paper, we propose a privacy preservation algorithm based on LKC-privacy model to protect the privacy attacks of trajectory data publishing. Not only can the data utility be maintained effectively by the data generalisation approach, but this algorithm can also reduce the computing time of the look-up table creation which is one of the highest computational processes. Our proposed algorithm is evaluated with extensive experiments. From the experiments, the enhanced-LT algorithm performance in terms of the execution time outperforms the baseline LT algorithm by 48.5% on average. The results show that our proposed algorithm returns not only the optimal solution but also highly efficient computation times.

Keywords: LKC-privacy; trajectory data publishing; optimal algorithm; generalisation technique.

DOI: 10.1504/IJGUC.2023.135308

International Journal of Grid and Utility Computing, 2023 Vol.14 No.6, pp.632 - 643

Received: 25 Dec 2021
Accepted: 27 Jan 2022
Published online: 05 Dec 2023 *

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