Abstract
Trajectory data of mobile users contain plenty of sensitive spatial and temporal information, and can support many applications through data analysing and mining. However, re-identification attack and inference attack on such data may cause serious personal privacy leakage. Existing privacy preserving techniques cannot protect trajectory privacy well or largely scarify data utility. In view of these issues, in this paper we propose an enhanced trajectory privacy preserving method which can protect the trajectory privacy preferably while maintaining a high utility of the trajectory in data publishing. A mechanism is proposed to protect the privacy through replacing stop points in the trajectory and an effective trajectory reconstruction algorithm is introduced to avoid the mutations of trajectory, and also deal with the possible presence of obstacles around trajectories. The performance of our proposal is comprehensively evaluated on a real trajectory dataset. The results show that our method achieves a high privacy level and improves the utility of trajectory data greatly, compared with the state-of-the-art method.
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Acknowledgments
This work is supported by the National Natural Science Foundation of China (grants No. 61672133 and No. 61632007), the Fundamental Research Funds for the Central Universities (grants No. ZYGX2015J058 and No. ZYGX2014Z007), and a project funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions and Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology.
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Dai, Y., Shao, J. (2017). An Enhanced Method of Trajectory Privacy Preservation Through Trajectory Reconstruction. In: Sun, X., Chao, HC., You, X., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2017. Lecture Notes in Computer Science(), vol 10603. Springer, Cham. https://doi.org/10.1007/978-3-319-68542-7_19
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DOI: https://doi.org/10.1007/978-3-319-68542-7_19
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