Abstract
The next generation of location-based services has been being predicted to achieve its superior development over the coming years. Keeping pace with this growth are new trends of predictive applications emerging to meet the demands of end-users and satisfy their matters of life. The violation of users’ private information from their position disclosure, however, cuts off their beliefs when they enjoy such services. In this paper, therefore, we propose a novel index structure known as PPST-tree, which is able to deal with predictive and aggregate queries and is aware of trajectory privacy protection towards future positions of moving objects. Moreover, the prediction model and related strategies are also introduced in order to support location-based applications whereas user privacy is still preserved. Last but not least, privacy analyses and performance experiments show how well the proposed method can help.
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Phan, T.N., Dang, T.K. (2012). A Novel Trajectory Privacy-Preserving Future Time Index Structure in Moving Object Databases. In: Nguyen, NT., Hoang, K., Jȩdrzejowicz, P. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2012. Lecture Notes in Computer Science(), vol 7653. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34630-9_13
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DOI: https://doi.org/10.1007/978-3-642-34630-9_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34629-3
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