Abstract
Mobility traces include both spatial and temporal aspects of individuals’ movement processes. As a result, these traces are among the most sensitive data that could be exploited to uniquely identify an individual. In this paper, we propose a spatio-temporal mobility model that extends a purely spatial Markov mobility model to effectively tackle the identification problem. The idea is to incorporate temporal perspectives of mobility traces into that probabilistic spatial mobility model to make it more specific for an individual with respect to both space and time. Then we conduct experiments to evaluate the degree to which individuals can be uniquely identified using our spatio-temporal mobility model. The results show that the proposed model outperforms the purely spatial one on the benchmark of MIT Reality Mining project dataset.
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Pham, N., Cao, T. (2014). A Spatio-Temporal Profiling Model for Person Identification. In: Huynh, V., Denoeux, T., Tran, D., Le, A., Pham, S. (eds) Knowledge and Systems Engineering. Advances in Intelligent Systems and Computing, vol 244. Springer, Cham. https://doi.org/10.1007/978-3-319-02741-8_31
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DOI: https://doi.org/10.1007/978-3-319-02741-8_31
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