Abstract
Traditional trajectory k-anonymity method might lead to a serious information distortion of trajectory and reduce the data quality. This paper proposes an efficient method to protect trajectory privacy by protecting points of interest, and improve the data quality.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Gruteser, M., Grunwald, D.: Anonymous usage of location-based services through spatial and temporal cloaking. In: Proceedings of the 1st International Conference on Mobile Systems, Applications and Services, pp. 31–42. ACM (2003)
Abul, O., Bonchi, F., Nanni, M.: Anonymization of moving objects databases by clustering and perturbation. Information Systems 35(8), 884–910 (2010)
Nergiz, M.E., Atzori, M., Saygin, Y.: Towards trajectory anonymization: a generalization-based approach. In: Proceedings of the SIGSPATIAL ACM GIS International Workshop on Security and Privacy in GIS and LBS, pp. 52–61. ACM (2008)
Monreale, A., Andrienko, G.L., Andrienko, N.V., et al.: Movement Data Anonymity through Generalization. Transactions on Data Privacy 3(2), 91–121 (2010)
Yarovoy, R., Bonchi, F., Lakshmanan, L.V.S., et al.: Anonymizing moving objects: how to hide a MOB in a crowd?. In: Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology, pp. 72–83. ACM (2009)
Huo, Z, Huang, Y, Meng, X.: History trajectory privacy-preserving through graph partition. In: Proceedings of the 1st International Workshop on Mobile Location-Based Service, pp. 71–78. ACM (2011)
Huo, Z., Meng, X., Hu, H., Huang, Y.: You can walk alone: trajectory privacy-preserving through significant stays protection. In: Lee, S.-g., Peng, Z., Zhou, X., Moon, Y.-S., Unland, R., Yoo, J. (eds.) DASFAA 2012, Part I. LNCS, vol. 7238, pp. 351–366. Springer, Heidelberg (2012)
Zheng, Y, Zhang, L, Xie, X, et al.: Mining interesting locations and travel sequences from GPS trajectories. In: Proceedings of the 18th International Conference on World Wide Web, pp. 791–800. ACM (2009)
Birant, D., Kut, A.: ST-DBSCAN: An algorithm for clustering spatial–temporal data. Data & Knowledge Engineering 60(1), 208–221 (2007)
Domingo-Ferrer, J, Sramka, M, Trujillo-Rasúa, R.: Privacy-preserving publication of trajectories using microaggregation. In: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Security and Privacy in GIS and LBS, pp. 26–33. ACM (2010)
Machanavajjhala, A., Kifer, D., Gehrke, J., et al.: l-diversity: Privacy beyond k-anonymity. ACM Transactions on Knowledge Discovery from Data (TKDD) 1(1), 3 (2007)
Trajcevski, G., Wolfson, O., Hinrichs, K., et al.: Managing uncertainty in moving objects databases. ACM Transactions on Database Systems (TODS) 29(3), 463–507 (2004)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Zhang, Z., Sun, Y., Xie, X., Pan, H. (2015). An Efficient Method on Trajectory Privacy Preservation. In: Wang, Y., Xiong, H., Argamon, S., Li, X., Li, J. (eds) Big Data Computing and Communications. BigCom 2015. Lecture Notes in Computer Science(), vol 9196. Springer, Cham. https://doi.org/10.1007/978-3-319-22047-5_19
Download citation
DOI: https://doi.org/10.1007/978-3-319-22047-5_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-22046-8
Online ISBN: 978-3-319-22047-5
eBook Packages: Computer ScienceComputer Science (R0)