Articles producció científica> Enginyeria Informàtica i Matemàtiques

Generation of Synthetic Trajectory Microdata from Language Models

  • Identification data

    Identifier: imarina:9282083
  • Authors:

    A. Blanco-Justicia, N. Jebreel, J. A. Manjón and J. Domingo-Ferrer
  • Others:

    Author, as appears in the article.: A. Blanco-Justicia, N. Jebreel, J. A. Manjón and J. Domingo-Ferrer
    Department: Enginyeria Informàtica i Matemàtiques
    URV's Author/s: Blanco Justicia, Alberto / Domingo Ferrer, Josep / Jebreel, Najeeb Moharram Salim / Manjón Paniagua, Jesús Alberto
    Project code: 101006879
    Abstract: Releasing and sharing mobility data, and specifically trajectories, is necessary for many applications, from infrastructure planning to epidemiology. Yet, trajectories are highly sensitive data, because the points visited by an individual can be identifying and also confidential. Hence, trajectories must be anonymized before releasing or sharing them. While most contributions to the trajectory anonymization literature take statistical approaches, deep learning is increasingly being used. We observe that natural language sentences and trajectories share a sequential nature that can be exploited in similar ways. In this paper, we present preliminary work on generating synthetic trajectories using machine learning models typically used for natural language processing. Our empirical results attest to the quality of the generated synthetic trajectories. Furthermore, our methods allow discovering natural neighborhoods based on trajectories.
    licence for use: https://creativecommons.org/licenses/by/3.0/es/
    Author's mail: najeeb.jebreel@urv.cat alberto.blanco@urv.cat josep.domingo@urv.cat jesus.manjon@urv.cat jesus.manjon@urv.cat jesus.manjon@urv.cat najeeb.jebreel@urv.cat
    Author identifier: 0000-0002-1108-8082 0000-0001-7213-4962 0000-0003-3513-8109 0000-0003-3513-8109 0000-0003-3513-8109
    Record's date: 2023-11-19
    Papper version: info:eu-repo/semantics/acceptedVersion
    Link to the original source: https://link.springer.com/chapter/10.1007/978-3-031-13945-1_13
    Funding program: Horizon 2020
    Licence document URL: http://repositori.urv.cat/ca/proteccio-de-dades/
    Papper original source: Lecture Notes In Computer Science. 13463 172-187
    APA: A. Blanco-Justicia, N. Jebreel, J. A. Manjón and J. Domingo-Ferrer (2022). Generation of Synthetic Trajectory Microdata from Language Models. Lecture Notes In Computer Science, 13463(), 172-187
    Acronym: MOBIDATALAB
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2022
    Funding program action: Labs for prototyping future Mobility Data sharing cloud solutions
    Publication Type: Journal Publications
  • Keywords:

    Privacy; Synthetic data generation; Mobility data
  • Documents:

  • Cerca a google

    Search to google scholar