  With the increasing amount of mobility data being collected on a daily basis
by location-based services (LBSs) comes a new range of threats for users,
related to the over-sharing of their location information. To deal with this
issue, several location privacy protection mechanisms (LPPMs) have been
proposed in the past years. However, each of these mechanisms comes with
different configuration parameters that have a direct impact both on the
privacy guarantees offered to the users and on the resulting utility of the
protected data. In this context, it can be difficult for non-expert system
designers to choose the appropriate configuration to use. Moreover, these
mechanisms are generally configured once for all, which results in the same
configuration for every protected piece of information. However, not all users
have the same behaviour, and even the behaviour of a single user is likely to
change over time. To address this issue, we present in this paper ALP, a new
framework enabling the dynamic configuration of LPPMs. ALP can be used in two
scenarios: (1) offline, where ALP enables a system designer to choose and
automatically tune the most appropriate LPPM for the protection of a given
dataset; (2) online, where ALP enables the user of a crowd sensing application
to protect consecutive batches of her geolocated data by automatically tuning
an existing LPPM to fulfil a set of privacy and utility objectives. We evaluate
ALP on both scenarios with two real-life mobility datasets and two
state-of-the-art LPPMs. Our experiments show that the adaptive LPPM
configurations found by ALP outperform both in terms of privacy and utility a
set of static configurations manually fixed by a system designer.
