  In recent years, the widespread of mobile devices equipped with GPS and
communication chips has led to the growing use of location-based services (LBS)
in which a user receives a service based on his current location. The
disclosure of user's location, however, can raise serious concerns about user
privacy in general, and location privacy in particular which led to the
development of various location privacy-preserving mechanisms aiming to enhance
the location privacy while using LBS applications. In this paper, we propose to
model the user mobility pattern and utility of the LBS as a Markov decision
process (MDP), and inspired by probabilistic current state opacity notation, we
introduce a new location privacy metric, namely $\epsilon-$privacy, that
quantifies the adversary belief over the user's current location. We exploit
this dynamic model to design a LPPM that while it ensures the utility of
service is being fully utilized, independent of the adversary prior knowledge
about the user, it can guarantee a user-specified privacy level can be achieved
for an infinite time horizon. The overall privacy-preserving framework,
including the construction of the user mobility model as a MDP, and design of
the proposed LPPM, are demonstrated and validated with real-world experimental
data.
