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Mobility, Data Mining and Privacy the Experience of the GeoPKDD Project

  • Fosca Giannotti
  • Dino Pedreschi
  • Franco Turini
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5456)

Abstract

Our everyday actions, the way people live and move, leave digital traces in the information systems of the organizations that provide services through the wireless networks for mobile communication. The potential value of these traces in recording the human activities in a territory is becoming real, because of the increasing pervasiveness and positioning accuracy. The number of mobile phone users worldwide was recently estimated as 3 billion, i.e., one mobile phone every two people. On the other hand, the location technologies, such as GSM and UMTS, currently used by wireless phone operators are capable of providing an increasingly better estimate of a user’s location, while the integration of various positioning technologies proceeds: GPS-equipped mobile devices can transmit their trajectories to some service provider (and the European satellite positioning system Galileo may improve precision and pervasiveness in the near future), Wi-Fi and Bluetooth devices may be a source of data for indoor positioning, Wi-Max can become an alternative for outdoor positioning, and so on. The consequence of this scenario, where communication and computing devices are ubiquitous and carried everywhere and always by people and vehicles, is that human activity in a territory may be sensed – not necessarily on purpose, but simply as a side effect of the ubiquitous services provided to mobile users. Thus, the wireless phone network, designed to provide mobile communication, can also be viewed as an infrastructure to gather mobility data, if used to record the location of its users at different times. The wireless networks, whose pervasiveness and localization precision increase while new location-based and context-based services are offered to mobile users, are becoming the nerves of our territory – in particular, our towns – capable of sensing and, possibly, recording our movements.

Keywords

Data Mining Mobile User Mobility Data Mobile Phone User Geographic Knowledge 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

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    Giannotti, F., Pedreschi, D. (eds.): Mobility, Data Mining and Privacy. Springer, Heidelberg (2008)Google Scholar
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    Verykios, V.S., Damiani, M.L., Gkoulalas-Divanis, A.: Privacy and Security in Spatiotemporal Data and Trajectories. In: Pedreschi, D., Giannotti, F. (eds.) Mobility, Data Mining and Privacy, pp. 213–240. Springer, Heidelberg (2008)CrossRefGoogle Scholar
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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Fosca Giannotti
    • 1
  • Dino Pedreschi
    • 1
  • Franco Turini
    • 1
  1. 1.KDD Lab ISTI-CNR, Pisa, and University of PisaItaly

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