Towards trajectory anonymisation using multi-dimensional index structures

Ahmed Almasrahi, Heechang Shin and Haibing Lu

International Journal of Business Continuity and Risk Management, 2016, vol. 6, issue 4, 304-313

Abstract: Trajectory datasets are increasingly available due to the technological advances in location-sensing devices, wireless technologies, and hand-held devices. However, the datasets also causes consumer privacy concerns. This paper addresses the privacy issues by using the internal structure of R-tree, a multi-dimensional index structure. The benefit of using R-tree is that it clusters trajectories in a way that their bounding spatiotemporal extension is minimised, thus achieving better quality in the anonymised database. This is a desirable property of the resulting anonymised database. In order to improve the quality of service requirements, a novel algorithm has been proposed.

Keywords: LBS; loction-based services; k-anonymity; security; privacy protection; privacy preservation; trajectory anonymisation; R-tree; multidimensional index structures; clustering; quality of service; QoS. (search for similar items in EconPapers)
Date: 2016
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