Abstract
With the increasing popularity of mobile communication devices loaded with positioning capabilities (e.g.,GPS), there is growing demand for enjoying location-based services (LBSs). An important problem in LBSs is the disclosure of a user’s real location while interacting with the location service provider (LSP). To address this issue, existing solutions generally introduce a trusted Anonymizer between the users and the LSP. But the introduction of an Anonymizer actually transfers the security risks from the LSP to the Anonymizer. Once the Anonymizer is compromised, it may put the user information in jeopardy. In this paper, we propose an enhanced location privacy preserving (ELPP) scheme for the LBS environment. Our scheme employs an entity, termed Function Generator, to distribute the spatial transformation parameters periodically, with which the users and the LSP can performs the mutual transformation between a real location and a pseudo location. Without the transforming parameters, the Anonymizer cannot have any knowledge about a user’s real location. The main merits of our scheme include (1) no fully trusted entities are required; (2) each user can obtain accurate POIs, while preserving location privacy.
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
Lu, R., Lin, X., Liang, X., Shen, X.: A dynamic privacy-preserving key management scheme for location-based services in vanets. IEEE Transactions on Intelligent Transportation Systems 13(1), 127–139 (2012)
Shin, K.G., Ju, X., Chen, Z., Hu, X.: Privacy protection for users of location-based services. IEEE Wireless Communications 19(1), 30–39 (2012)
Pingley, A., Zhang, N., Fu, X., Choi, H.A., Subramaniam, S., Zhao, W.: Protection of query privacy for continuous location based services. In: Proceedings IEEE INFOCOM, pp. 1710–1718 (2011)
Yiu, M.L., Jensen, C.S., Huang, X., Lu, H.: Spacetwist: Managing the trade-offs among location privacy, query performance, and query accuracy in mobile services. In: Proceedings of IEEE ICDE, pp. 366–375 (2008)
Vu, K., Zheng, R., Gao, J.: Efficient algorithms for k-anonymous location privacy in participatory sensing. In: Proceedings IEEE INFOCOM, pp. 2399–2407 (2012)
Ghinita, G., Zhao, K., Papadias, D., Kalnis, P.: A reciprocal framework for spatial k-anonymity. Information Systems 35(3), 299–314 (2010)
Pan, X., Xu, J., Meng, X.: Protecting location privacy against location-dependent attacks in mobile services. IEEE Transactions on Knowledge and Data Engineering 24(8), 1506–1519 (2012)
Gruteser, M., Grunwald, D.: Anonymous usage of location-based services through spatial and temporal cloaking. In: Proceedings of ACM MobiSys, pp. 31–42 (2003)
Hilbert, D.: Ueber die stetige abbildung einer line auf ein flchenstuck. Mathematische Annalen 38(3), 459–460 (1891)
Ghinita, G., Kalnis, P., Khoshgozaran, A., Shahabi, C., Tan, K.L.: Private queries in location based services: anonymizers are not necessary. In: Proceedings of ACM SIGMOD, pp. 121–132 (2008)
Solanas, A., Domingo-Ferrer, J., Martínez-Ballesté, A.: Location privacy in location-based services: Beyond ttp-based schemes. In: Proceedings of PILBA, pp. 12–23 (2008)
Domingo-Ferrer, J.: Microaggregation for database and location privacy. In: Etzion, O., Kuflik, T., Motro, A. (eds.) NGITS 2006. LNCS, vol. 4032, pp. 106–116. Springer, Heidelberg (2006)
Sweeney, L.: k-anonymity: A model for protecting privacy. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 10(5), 557–570 (2002)
Gedik, B., Liu, L.: Location privacy in mobile systems: A personalized anonymization model. In: Proceedings of IEEE ICDCS, pp. 620–629 (2005)
Kalnis, P., Ghinita, G., Mouratidis, K., Papadias, D.: Preventing location-based identity inference in anonymous spatial queries. IEEE Transactions on Knowledge and Data Engineering 19(12), 1719–1733 (2007)
Khoshgozaran, A., Shahabi, C.: Blind evaluation of nearest neighbor queries using space transformation to preserve location privacy. In: Papadias, D., Zhang, D., Kollios, G. (eds.) SSTD 2007. LNCS, vol. 4605, pp. 239–257. Springer, Heidelberg (2007)
Hu, H., Lee, D.L.: Range nearest-neighbor query. IEEE Transactions on Knowledge and Data Engineering 18(1), 78–91 (2006)
Liu, Q., Tan, C.C., Wu, J., Wang, G.: Cooperative private searching in clouds. Journal of Parallel and Distributed Computing 13(1), 1019–1031 (2012)
Green, M., Ateniese, G.: Identity-based proxy re-encryption. In: Katz, J., Yung, M. (eds.) ACNS 2007. LNCS, vol. 4521, pp. 288–306. Springer, Heidelberg (2007)
Lawder, J.K.: Calculation of mappings between one and n-dimensional values using the hilbert space-filling curve. School of Computer Science and Information Systems (2000)
Liu, X., Schrack, G.: Encoding and decoding the hilbert order. Software: Practice and Experience 26(12), 1335–1346 (1996)
Moon, B., Jagadish, H.V., Faloutsos, C., Saltz, J.H.: Analysis of the clustering properties of the hilbert space-filling curve. IEEE Transactions on Knowledge and Data Engineering 13(1), 124–141 (2001)
Lee, D.T.: On k-nearest neighbor voronoi diagrams in the plane. IEEE Transactions on Computers 100(6), 478–487 (1982)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer International Publishing Switzerland
About this paper
Cite this paper
Peng, T., Liu, Q., Wang, G. (2013). Privacy Preserving for Location-Based Services Using Location Transformation. In: Wang, G., Ray, I., Feng, D., Rajarajan, M. (eds) Cyberspace Safety and Security. CSS 2013. Lecture Notes in Computer Science, vol 8300. Springer, Cham. https://doi.org/10.1007/978-3-319-03584-0_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-03584-0_2
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03583-3
Online ISBN: 978-3-319-03584-0
eBook Packages: Computer ScienceComputer Science (R0)