Abstract
Publication of moving objects’ everyday life trajectories may cause serious personal privacy leakage. Existing trajectory privacy-preserving methods try to anonymize k whole trajectories together, which may result in complicated algorithms and extra information loss. We observe that, background information are more relevant to where the moving objects really visit rather than where they just pass by. In this paper, we propose an approach called You Can Walk Alone (YCWA) to protect trajectory privacy through generalization of stay points on trajectories. By protecting stay points, sensitive information is protected, while the probability of whole trajectories’ exposure is reduced. Moreover, the information loss caused by the privacy-preserving process is reduced. To the best of our knowledge, this is the first research that protects trajectory privacy through protecting significant stays or similar concepts. At last, we conduct a set of comparative experimental study on real-world dataset, the results show advantages of our approach.
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References
Nergiz, M.E., Atzori, M., Saygin, Y., Baris, G.: Towards Trajectory Anonymization: A Generalization-based Approach. IEEE Transactions on Data Privacy 2, 47–75 (2009)
Abul, O., Bonchi, F., Nanni, M.: Never Walk Alone: Uncertainty for Anonymity in Moving Objects Databases. In: 24th IEEE International Conference on Data Engineering, pp. 215–226. IEEE Press, Washington (2008)
Yarovoy, R., Bonchi, F., Lakshmanan, S., Wang, W.H.: Anonymizing Moving Objects: How to Hide a MOB in a Crowd? In: 12th International Conference on Extending Database Technology, pp. 72–83. ACM Press, New York (2009)
Terrovitis, M., Mamoulis, N.: Privacy Preservation in the Publication of Trajectories. In: 9th International Conference on Mobile Data Management, pp. 65–72. IEEE Press, Washington (2008)
Gidofalvi, G., Huang, X., Bach, P.T.: Privacy-preserving Trajectory Collection. In: 16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, p. 46. ACM Press, New York (2008)
Moreale, A., Andrienko, G.L., Andrienko, N.V., Giannotti, F., Pedreschi, D., Rinzivillo, S., Wrobel, S.: Movement Data Anonymity through Generalization. IEEE Transactions on Data Privacy 3, 91–121 (2010)
Zheng, Y., Zhang, L., Xie, X., Ma, W.: Mining Interesting Locations and Travel Sequences from GPS Trajectories. In: 18th International Conference on World Wide Web, pp. 791–800. ACM Press, New York (2009)
Cao, X., Cong, G., Jensen, C.S.: Mining Significant Semantic Locations From GPS Data. Proceedings of the VLDB Endowment 3(1), 1009–1020 (2010)
Google Reverse Geo-coder, http://www.findlatitudeandlongitude.com/find-address-from-latitude-and-longitude.php
Gruteser, M., Liu, X.: Protecting Privacy in Continuous Location-tracking Applications. IEEE Security and Privacy 2(2), 28–34 (2004)
Microsoft Research Geolife, http://research.microsoft.com/en-us/projects/geolife/
Kaufmann, L., Rousseeuw, P.: Clustering by means of medoids. Statistical Data Analysis Based on the L1-Norm and Related Methods, 405–416 (1987)
Xu, T., Cai, Y.: Exploring Historical Location Data for Anonymity Preservation in Location-based Services. In: 27th Conference on Computer Communications, pp. 547–555. IEEE Press, Washington (2008)
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Huo, Z., Meng, X., Hu, H., Huang, Y. (2012). You Can Walk Alone: Trajectory Privacy-Preserving through Significant Stays Protection. In: Lee, Sg., Peng, Z., Zhou, X., Moon, YS., Unland, R., Yoo, J. (eds) Database Systems for Advanced Applications. DASFAA 2012. Lecture Notes in Computer Science, vol 7238. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29038-1_26
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DOI: https://doi.org/10.1007/978-3-642-29038-1_26
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