Title: Towards trajectory anonymisation using multi-dimensional index structures

Authors: Ahmed Almasrahi; Heechang Shin; Haibing Lu

Addresses: Department of Information Systems, Hagan School of Business, Iona College, 715 North Avenue, New Rochelle, NY 10801, USA ' Department of Information Systems, Hagan School of Business, Iona College, 715 North Avenue, New Rochelle, NY 10801, USA ' Department of Operations Management and Information Systems, The Leavey School of Business, Santa Clara University, 500 El Camino Real, Santa Clara, California 95053, USA

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.

DOI: 10.1504/IJBCRM.2016.081258

International Journal of Business Continuity and Risk Management, 2016 Vol.6 No.4, pp.304 - 313

Available online: 28 Dec 2016 *

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