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countries worldwide. In particular, semantic trajectories magnify the risk for
privacy because behavior information on individuals is explicitly extracted and
represented in a machine-readable form, and therefore can be used within infor-
mation processing applications and easily unfolded to third parties. Though
fundamental, privacy regulations are not capable of preventing malicious and
curious parties from improperly accessing and using collected data. This instead
is the goal of location PETs (privacy-enhancing technologies). In general, loca-
tion PETs can be applied at two different stages:
1. Before position data are collected. In this case the goal of location PETs
is to prevent mobility data collectors from obtaining the exact location and
trace of individuals, everytime and everywhere. Because these techniques
are applied on the fly, we refer to this form of protection as online location
privacy .
2. After position data are collected and trajectories reconstructed. The goal of
location PETs is to shape trajectory data in a way that the data set can be
published or released to some other partywithout incurring privacy violations.
We refer to this as offline location privacy .
Offline and online location privacy present different requirements, which call
for different solutions. In particular, the solutions for the online protection of
location privacy have to deal with incomplete knowledge of the individuals'
trajectories (usually only the current and past positions are known); moreover,
techniques must be efficient so as not to compromise the effectiveness of data
collection. In what follows, we survey major paradigms supporting online loca-
tion privacy while techniques for offline location privacy will be presented later
on in Chapter 9 .
2.5.1 Online Location Privacy
Research on position privacy took off early last decade with the emergence of
mobile applications enabling the tracking of moving objects, for example, the
vehicles monitored by a fleet management system, and location-based services
(LBS), for example, search of points of interests nearby. These applications
typically rely on a client-server architecture: the position is collected by mobile
devices (the clients) and conveyed to a server handled by a service provider. In
this scenario, service providers are in the position of collecting large amounts of
position data, therefore, if they are disrespectful of users' rights and requirements
or, simply, if the collected data are stolen, users' privacy is at stake. Commonly,
location PETs seek to limit the transmission of either accurate or explicit loca-
tion information to service providers. These techniques can be further classified
based on the information to be protected, that is, the privacy goals. In particular,
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