Volume 17 Number 2 (Mar. 2022)
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JSW 2022 Vol.17(2): 71-86 ISSN: 1796-217X
doi: 10.17706/jsw.17.2.71-86

Trajectory Privacy Protection Mechanism based on Salp-Like Swarm Algorithm

Sitong Shi, Jing Zhang*, Yanzi Li, Jianyu Hu
School of Computer Science and Mathematics, Fujian Provincial Key Laboratory of Big Data Mining and Applications, Institute of Artificial Intelligence, Fujian University of Technology, Fuzhou, China.

Abstract—Location-based services have been widely used in daily life, providing diversified services for users. However, users may face the risk of trajectory privacy disclosure while enjoying the convenience of location-based services. Most of the existing trajectory protection schemes cannot match the road network and are vulnerable to attacks based on background information. In this paper, the concept of salp swarm algorithm is introduced to construct salp-like swarm algorithm, which can generate K−1 false trajectories that are highly similar to real trajectories. It is difficult for attackers to distinguish them. Besides, a road network matching model is designed in order to match the proposed trajectory privacy protection algorithm with the real road network environment, so that the effect of trajectory privacy protection is improved. Morever, a false location selection mechanism is proposed to find false location points, which not only considers the location and speed of users, but also ensures that the selection of false location points is more in line with the road network environment. The experimental results show that, under the condition of satisfying the same service quality, the trajectory privacy leakage probability of this scheme is reduced by 33% compared with the existing schemes, and it has better privacy protection effect.

Index Terms—K-anonymous, Location-based services (LBSs), salp-like swarm algorithm (SLSA), trajectory privacy.


Cite: Sitong Shi, Jing Zhang*, Yanzi Li, Jianyu Hu, "Trajectory Privacy Protection Mechanism based on Salp-Like Swarm Algorithm," Journal of Software vol. 17, no. 2, pp.71-86, 2022.

Copyright © 2022 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

ISSN: 1796-217X (Online)
Frequency:  Quarterly
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, CNKIGoogle Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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