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An Effective Privacy Architecture to Preserve User Trajectories in Reward-Based LBS Applications

Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen 518055, China
College of Mathematics and Computer Science, Fujian Normal University, Fuzhou 350117, China
Authors to whom correspondence should be addressed.
Part of this manuscript was presented and published at the 6th International Conference on Communication and Network Security, Singapore, 26–29 November 2016.
ISPRS Int. J. Geo-Inf. 2018, 7(2), 53;
Received: 11 December 2017 / Revised: 25 January 2018 / Accepted: 5 February 2018 / Published: 7 February 2018
PDF [878 KB, uploaded 8 February 2018]


How can training performance data (e.g., running or walking routes) be collected, measured, and published in a mobile program while preserving user privacy? This question is becoming important in the context of the growing use of reward-based location-based service (LBS) applications, which aim to promote employee training activities and to share such data with insurance companies in order to reduce the healthcare insurance costs of an organization. One of the main concerns of such applications is the privacy of user trajectories, because the applications normally collect user locations over time with identities. The leak of the identified trajectories often results in personal privacy breaches. For instance, a trajectory would expose user interest in places and behaviors in time by inference and linking attacks. This information can be used for spam advertisements or individual-based assaults. To the best of our knowledge, no existing studies can be directly applied to solve the problem while keeping data utility. In this paper, we identify the personal privacy problem in a reward-based LBS application and propose privacy architecture with a bounded perturbation technique to protect user’s trajectory from the privacy breaches. Bounded perturbation uses global location set (GLS) to anonymize the trajectory data. In addition, the bounded perturbation will not generate any visiting points that are not possible to visit in real time. The experimental results on real-world datasets demonstrate that the proposed bounded perturbation can effectively anonymize location information while preserving data utility compared to the existing methods. View Full-Text
Keywords: privacy architecture; identified trajectory; anonymization; data utility; location-based service privacy architecture; identified trajectory; anonymization; data utility; location-based service

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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).

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Hasan, A.S.M.T.; Qu, Q.; Li, C.; Chen, L.; Jiang, Q. An Effective Privacy Architecture to Preserve User Trajectories in Reward-Based LBS Applications. ISPRS Int. J. Geo-Inf. 2018, 7, 53.

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