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

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dc.contributor University of Helsinki, Helsinki Institute for Information Technology en
dc.contributor University of Helsinki, Department of Computer Science en
dc.contributor.author Jitta, Aditya
dc.contributor.author Klami, Arto
dc.date.accessioned 2019-08-06T21:33:55Z
dc.date.available 2021-02-11T03:46:04Z
dc.date.issued 2017-11-29
dc.identifier.citation 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 en
dc.identifier.issn 0925-2312
dc.identifier.other PURE: 58316795
dc.identifier.other PURE UUID: 82dbd8c9-1faa-45f4-a57a-a6dc1addc311
dc.identifier.other WOS: 000408183900019
dc.identifier.other Scopus: 85019673720
dc.identifier.other ORCID: /0000-0002-7950-1355/work/39705785
dc.identifier.uri http://hdl.handle.net/10138/304289
dc.description.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. en
dc.format.extent 10
dc.language.iso eng
dc.relation.ispartof Neurocomputing
dc.rights en
dc.subject 112 Statistics and probability en
dc.subject Hierarchical Dirichlet process en
dc.subject Markov models en
dc.subject Movement trajectories en
dc.subject Nonparametric Bayesian inference en
dc.title Partially hidden Markov models for privacy-preserving modeling of indoor trajectories en
dc.type Article
dc.description.version Peer reviewed
dc.identifier.doi https://doi.org/10.1016/j.neucom.2017.05.035
dc.type.uri info:eu-repo/semantics/other
dc.type.uri info:eu-repo/semantics/acceptedVersion
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