Open Access Regular article

Luca Rossi1*, James Walker1 and Mirco Musolesi12

Author Affiliations

1 School of Computer Science, University of Birmingham, Birmingham B15 2TT, UK

2 Department of Geography, University College London, London WC1E 6BT, UK

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EPJ Data Science 2015, 4:11  doi:10.1140/epjds/s13688-015-0049-x

Published: 5 August 2015


One of the greatest concerns related to the popularity of GPS-enabled devices and applications is the increasing availability of the personal location information generated by them and shared with application and service providers. Moreover, people tend to have regular routines and be characterized by a set of “significant places”, thus making it possible to identify a user from his/her mobility data.

In this paper we present a series of techniques for identifying individuals from their GPS movements. More specifically, we study the uniqueness of GPS information for three popular datasets, and we provide a detailed analysis of the discriminatory power of speed, direction and distance of travel. Most importantly, we present a simple yet effective technique for the identification of users from location information that are not included in the original dataset used for training, thus raising important privacy concerns for the management of location datasets.


GPS; privacy; identification

Source: Abstract

Via: Google Alert for Data Science