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Chapter 5
SGUM-based Pseudonym Change
for Personalized Location Privacy
In this chapter, we study the application of the SGUM framework to pseudonym
change for personalized location privacy.
5.1
Introduction
With the rapid growth of mobile networks, location-based services (LBS) have
become increasingly popular recently (e.g., location-based navigation and recom-
mendation). However, the providers of LBSs are often considered not trustworthy,
due to the risk of leaking users' location information to other parties (e.g., sell users'
location data). As a result, mobile users are exposed to potential privacy threats when
using a LBS. Although a user can use a pseudonym for the LBS, an adversary can
infer the user's real identity from its location traces (e.g., from the user's home and
work addresses). To protect location privacy, an effective approach is to “confuse”
the adversary using the notion of anonymity [ 1 ]: mobile users in physical proximity
can change their pseudonyms simultaneously to form an anonymity set, so that the
adversary cannot distinguish any of them from the others.
A basic assumption commonly used in existing studies [ 2 , 3 , 4 ] is that all users
participating in pseudonym change have the same anonymity set. However, from an
individual user's perspective, the set of users that can obfuscate its pseudonym (i.e.,
its anonymity set) can be different from that of another user, depending on users'
physical locations. For example, a user with a higher level of privacy sensitivity can
have a smaller anonymity set than others. It is thus desirable to meet users' needs for
personalized location privacy . To this end, we consider a general anonymity model
where a user can define its specific anonymity set different from others' (as illustrated
in Fig. 5.1 ).
In this chapter, we leverage the social tie structure among mobile users to incen-
tivize them to participate in pseudonym change. To this end, we cast users' decision
making of whether to participate in pseudonym change as a SGUM game, based on
a general anonymity model that allows each user to have its specific anonymity set.
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