  Location data privacy has become a serious concern for users as Location
Based Services (LBSs) have become an important part of their life. It is
possible for malicious parties having access to geolocation data to learn
sensitive information about the user such as religion or political views.
Location Privacy Preserving Mechanisms (LPPMs) have been proposed by previous
works to ensure the privacy of the shared data while allowing the users to use
LBSs. But there is no clear view of which mechanism to use according to the
scenario in which the user makes use of a LBS. The scenario is the way the user
is using a LBS (frequency of reports, number of reports). In this paper, we
study the sensitivity of LPPMs on the scenario on which they are used. We
propose a framework to systematically evaluate LPPMs by considering an
exhaustive combination of LPPMs, attacks and metrics. Using our framework we
compare a selection of LPPMs including an improved mechanism that we introduce.
By evaluating over a variety of scenarios, we find that the efficacy (privacy,
utility, and robustness) of the studied mechanisms is dependent on the
scenario: for example the privacy of Planar Laplace geo-indistinguishability is
greatly reduced in a continuous scenario. We show that the scenario is
essential to consider when choosing an obfuscation mechanism for a given
application.
