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Introduction to Mobility Data Privacy

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Handbook of Mobile Data Privacy

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Abstract

The recent advances in mobile computing and positioning technologies have resulted in a tremendous increase to the amount and accuracy in which human location data can be collected and processed. Human mobility traces can be used to support a number of real-world applications spanning from urban planning and traffic engineering, to studying the spread of diseases and managing environmental pollution. At the same time, research studies have shown that individual mobility is highly predictable and mostly unique, thus information about individuals’ movement can be used by adversaries to re-identify them and to learn sensitive information about their whereabouts. To address such privacy concerns, a significant body of research has emerged in the last 15 years, studying privacy issues related to human mobility and location information, in a number of contexts and real-world applications. This work has led to the adoption of privacy laws worldwide, for location privacy protection, as well as to the proposal of novel privacy models and techniques for technically protecting user privacy, while maintaining data utility. This chapter provides an introduction to the field of mobility data privacy, discusses the emerging research directions, along with the real-world systems and applications that have been proposed.

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Authors and Affiliations

  1. IBM Watson Health Headquarters, Cambridge, MA, USA

    Aris Gkoulalas-Divanis

  2. Dipartimento di Informatica, University of Milan, Milan, Italy

    Claudio Bettini

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  1. Aris Gkoulalas-Divanis
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  2. Claudio Bettini
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Correspondence to Aris Gkoulalas-Divanis .

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Editors and Affiliations

  1. IBM Watson Health Headquarters, Cambridge, MA, USA

    Aris Gkoulalas-Divanis

  2. Dipartimento di Informatica, University of Milan, Milan, Italy

    Claudio Bettini

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Gkoulalas-Divanis, A., Bettini, C. (2018). Introduction to Mobility Data Privacy. In: Gkoulalas-Divanis, A., Bettini, C. (eds) Handbook of Mobile Data Privacy . Springer, Cham. https://doi.org/10.1007/978-3-319-98161-1_1

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  • DOI: https://doi.org/10.1007/978-3-319-98161-1_1

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