Articles producció científica> Enginyeria Informàtica i Matemàtiques

Decentralized k-anonymization of trajectories via privacy-preserving tit-for-tat

  • Identification data

    Identifier: imarina:9262243
    Authors:
    Domingo-Ferrer, JosepMartinez, SergioSanchez, David
    Abstract:
    Mobility data, and specifically trajectories, are used to monitor the mobility of the population and are crucial to improve public health, transportation, urban planning, economic planning, etc. However, trajectories are personally identifiable information and hence they should be anonymized before releasing them for secondary use. Anonymization cannot be limited to suppressing the metadata containing the subject's identity, because the origin, the destination and even the intermediate points of a trajectory may allow re-identifying the subject who followed it. Proper anonymization requires masking detailed spatiotemporal information. The standard approach to build anonymized data sets is centralized: the subjects send their original movement data to a controller, who takes care of producing an anonymized mobility data set. This requires subjects to blindly trust the controller. In this paper, we empower subjects with the ability to anonymize their trajectories locally by adhering to a privacy model in order to achieve formal privacy guarantees. After reviewing the state of the art, we motivate our choice of k-anonymity as a privacy model. We then set out to decentralize k-anonymity in a rational setting: a subject k-anonymizes her completed trajectory by aggregating with k−1 similar trajectories obtained from other (unknown) subjects. The latter trajectories are gathered via an anonymous and privacy-preserving tit-for-tat data exchange protocol, which runs on a fully decentralized peer-to-peer network. Experiments show that, without relying on a (trusted) data controller and while ensuring privacy w.r.t. other peers, our approach yields k-anonymized mobility data sets that are still reasonably useful compared to the near-optimal data sets obtained in the centralized ap
  • Others:

    Author, as appears in the article.: Domingo-Ferrer, Josep; Martinez, Sergio; Sanchez, David
    Department: Enginyeria Informàtica i Matemàtiques
    URV's Author/s: Domingo Ferrer, Josep / Martinez Lluis, Sergio / Sánchez Ruenes, David
    Keywords: Privacy P2p K-anonymity Decentralized anonymization
    Abstract: Mobility data, and specifically trajectories, are used to monitor the mobility of the population and are crucial to improve public health, transportation, urban planning, economic planning, etc. However, trajectories are personally identifiable information and hence they should be anonymized before releasing them for secondary use. Anonymization cannot be limited to suppressing the metadata containing the subject's identity, because the origin, the destination and even the intermediate points of a trajectory may allow re-identifying the subject who followed it. Proper anonymization requires masking detailed spatiotemporal information. The standard approach to build anonymized data sets is centralized: the subjects send their original movement data to a controller, who takes care of producing an anonymized mobility data set. This requires subjects to blindly trust the controller. In this paper, we empower subjects with the ability to anonymize their trajectories locally by adhering to a privacy model in order to achieve formal privacy guarantees. After reviewing the state of the art, we motivate our choice of k-anonymity as a privacy model. We then set out to decentralize k-anonymity in a rational setting: a subject k-anonymizes her completed trajectory by aggregating with k−1 similar trajectories obtained from other (unknown) subjects. The latter trajectories are gathered via an anonymous and privacy-preserving tit-for-tat data exchange protocol, which runs on a fully decentralized peer-to-peer network. Experiments show that, without relying on a (trusted) data controller and while ensuring privacy w.r.t. other peers, our approach yields k-anonymized mobility data sets that are still reasonably useful compared to the near-optimal data sets obtained in the centralized approach.
    Thematic Areas: Telecommunications Interdisciplinar Engineering, electrical & electronic Engenharias iv Engenharias iii Computer science, software, graphics, programming Computer science, software engineering Computer science, information systems Computer science, hardware & architecture Computer networks and communications Ciências biológicas i Ciências ambientais Ciência da computação
    licence for use: https://creativecommons.org/licenses/by/3.0/es/
    Author's mail: david.sanchez@urv.cat sergio.martinezl@urv.cat josep.domingo@urv.cat
    Author identifier: 0000-0001-7275-7887 0000-0002-3941-5348 0000-0001-7213-4962
    Record's date: 2024-10-12
    Papper version: info:eu-repo/semantics/publishedVersion
    Link to the original source: https://www.sciencedirect.com/science/article/pii/S0140366422001153
    Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
    Papper original source: Computer Communications. 190 57-68
    APA: Domingo-Ferrer, Josep; Martinez, Sergio; Sanchez, David (2022). Decentralized k-anonymization of trajectories via privacy-preserving tit-for-tat. Computer Communications, 190(), 57-68. DOI: 10.1016/j.comcom.2022.04.011
    Article's DOI: 10.1016/j.comcom.2022.04.011
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2022
    Publication Type: Journal Publications
  • Keywords:

    Computer Networks and Communications,Computer Science, Hardware & Architecture,Computer Science, Information Systems,Computer Science, Software Engineering,Computer Science, Software, Graphics, Programming,Engineering, Electrical & Electronic,Telecommunications
    Privacy
    P2p
    K-anonymity
    Decentralized anonymization
    Telecommunications
    Interdisciplinar
    Engineering, electrical & electronic
    Engenharias iv
    Engenharias iii
    Computer science, software, graphics, programming
    Computer science, software engineering
    Computer science, information systems
    Computer science, hardware & architecture
    Computer networks and communications
    Ciências biológicas i
    Ciências ambientais
    Ciência da computação
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