Mobility Data and Privacy - Mobility Data - page 174

9
Mobility Data and Privacy
Fosca Giannotti, Anna Monreale, and Dino Pedreschi
9.1 Introduction
Mobility data represent an invaluable source of information that can be recorded
thanks to mobile telecommunications and ubiquitous computing where the loca-
tions of mobile users are continuously sensed. However, the collection, storage,
and sharing of these movement data sets raise serious privacy concerns. In fact,
position data may reveal the mobility behavior of the people: where they are
going, where they live, where they work, their religion and so on. All this infor-
mation refers to the private personal sphere of a person and therefore the analysis
of mobility data may potentially reveal many facets of his or her private life. As
a consequence, these kinds of data have to be considered personal information
to be protected against undesirable and unlawful disclosure.
In the specific case of mobility scenarios, there exist two major different
contexts inwhich the location privacy problemhas to be taken into consideration:
online location-based services and offline data analysis context. In the first case,
a user communicates to a service provider his or her location to receive on-the-
fly a specific service. An example of LBS is find the closest point of interest
(POI) , where a POI could be a restaurant. Privacy issues in the context of online
location-based services have been already addressed in Chapter 2 . In the second
case, large amounts of mobility data are collected and can be used for offline
data mining analysis able to extract reliable knowledge useful to understand and
manage intelligent transportation, urban planning, and sustainable mobility, as
already highlighted in previous chapters.
Many PETs (privacy-enhancing technologies) for mobility data have been
proposed by the scientific community. The most representative methods are
presented in Section 9.3 of the present chapter by highlighting how the privacy
models initially proposed for relational databases (presented in Section 9.2 ),
are extended to spatio-temporal data. A common point of view among all these
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