Partially hidden Markov models for privacy-preserving modeling of indoor trajectories

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http://hdl.handle.net/10138/304289

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Jitta , A & Klami , A 2017 , ' Partially hidden Markov models for privacy-preserving modeling of indoor trajectories ' , Neurocomputing , vol. 266 , pp. 196-205 . https://doi.org/10.1016/j.neucom.2017.05.035

Julkaisun nimi: Partially hidden Markov models for privacy-preserving modeling of indoor trajectories
Tekijä: Jitta, Aditya; Klami, Arto
Muu tekijä: University of Helsinki, Helsinki Institute for Information Technology
University of Helsinki, Department of Computer Science
Päiväys: 2017-11-29
Kieli: eng
Sivumäärä: 10
Kuuluu julkaisusarjaan: Neurocomputing
ISSN: 0925-2312
URI: http://hdl.handle.net/10138/304289
Tiivistelmä: Markov models are natural tools for modeling trajectories, following the principle that recent location history is predictive of near-future directions. In this work we study Markov models for describing and predicting human movement in indoor spaces, with the goal of modeling the movement on a coarse scale to protect the privacy of the individuals. Modern positioning devices, however, provide location information on a much more finer scale. To utilize this additional information we develop a novel family of partially hidden Markov models that couple each observed state with an auxiliary side information vector characterizing the movement within the coarse grid cell. We implement the model as a non-parametric Bayesian model and demonstrate it on real-world trajectory data collected in a hypermarket.
Avainsanat: 112 Statistics and probability
Hierarchical Dirichlet process
Markov models
Movement trajectories
Nonparametric Bayesian inference
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