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

Title: Partially hidden Markov models for privacy-preserving modeling of indoor trajectories
Author: Jitta, Aditya; Klami, Arto
Contributor organization: Helsinki Institute for Information Technology
Department of Computer Science
Multi-source probabilistic inference research group / Arto Klami
Date: 2017-11-29
Language: eng
Number of pages: 10
Belongs to series: Neurocomputing
ISSN: 0925-2312
DOI: https://doi.org/10.1016/j.neucom.2017.05.035
URI: http://hdl.handle.net/10138/304289
Abstract: 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.
Subject: 112 Statistics and probability
Hierarchical Dirichlet process
Markov models
Movement trajectories
Nonparametric Bayesian inference
Peer reviewed: Yes
Rights: cc_by_nc_nd
Usage restriction: openAccess
Self-archived version: acceptedVersion


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