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Longitudinal Collection and Analysis of Mobile Phone Data with Local Differential Privacy

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Privacy and Identity Management (Privacy and Identity 2020)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 619))

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Abstract

Longitudinal studies of human mobility could allow an understanding of human behavior on a vast scale. Mobile phone data call detail records (CDRs) have emerged as a prospective data source for such an important task. Nevertheless, there are significant risks when it comes to collecting this type of data, as human mobility has proven to be quite unique. Because CDRs are produced through the connection of mobile phones with mobile network operators’ (MNOs) antennas, it means that users cannot sanitize their data. Once MNOs intend to use such a data source for human mobility analysis, data protection authorities such as the CNIL (in France) recommends that data be sanitized on the fly instead of collecting raw data and publishing private output at the end of the analysis. Local differential privacy (LDP) mechanisms are currently applied during the process of data collection to preserve the privacy of users. In this paper, we propose an efficient privacy-preserving LDP-based methodology to collect and analyze multi-dimensional data longitudinally through mobile connections. In our proposal, rather than regarding users as unique IDs, we propose a generic scenario where one directly collects users’ sensitive data with LDP. The intuition behind this is collecting generic values, which can be generated by many users (e.g., gender), allowing a longitudinal study. As we show in the results, our methodology is very appropriate for this scenario, achieving accurate frequency estimation in a multi-dimensional setting while respecting some major recommendations of data protection authorities such as the GDPR and CNIL.

This work was supported by the Region of Bourgogne Franche-Comté CADRAN Project and by the EIPHI-BFC Graduate School (contract “ANR-17-EURE-0002”). The authors would also like to thank the Orange Application for Business team for their useful feedback and comments. Computations have been performed on the supercomputer facilities of “Mésocentre de Calcul de Franche-Comté”.

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Notes

  1. 1.

    https://github.com/hharcolezi/ldp-protocols-mobility-cdrs.

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Author information

Authors and Affiliations

  1. Femto-ST Institute, Univ. Bourg. Franche -Comté, UBFC, CNRS, Belfort, France

    Héber H. Arcolezi & Jean-François Couchot

  2. TICKET Lab., Antonine University, Hadat-Baabda, Lebanon

    Bechara Al Bouna

  3. School of Computing, National University of Singapore, Singapore, Singapore

    Xiaokui Xiao

Authors
  1. Héber H. Arcolezi
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  2. Jean-François Couchot
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  3. Bechara Al Bouna
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  4. Xiaokui Xiao
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Correspondence to Héber H. Arcolezi .

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Editors and Affiliations

  1. Fraunhofer Institute for Systems and Innovation Research ISI, Karlsruhe, Germany

    Michael Friedewald

  2. Université du Luxembourg, Esch-sur-Alzette, Luxembourg

    Stefan Schiffner

  3. AIT Austrian Institute of Technology, Vienna, Austria

    Stephan Krenn

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Arcolezi, H.H., Couchot, JF., Bouna, B.A., Xiao, X. (2021). Longitudinal Collection and Analysis of Mobile Phone Data with Local Differential Privacy. In: Friedewald, M., Schiffner, S., Krenn, S. (eds) Privacy and Identity Management. Privacy and Identity 2020. IFIP Advances in Information and Communication Technology, vol 619. Springer, Cham. https://doi.org/10.1007/978-3-030-72465-8_3

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  • DOI: https://doi.org/10.1007/978-3-030-72465-8_3

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