  Location-based services (LBSs) have become widely popular. Despite their
utility, these services raise concerns for privacy since they require sharing
location information with untrusted third parties. In this work, we study
privacy-utility trade-off in location sharing mechanisms. Existing approaches
are mainly focused on privacy of sharing a single location or myopic location
trace privacy; neither of them taking into account the temporal correlations
between the past and current locations. Although these methods preserve the
privacy for the current time, they may leak significant amount of information
at the trace level as the adversary can exploit temporal correlations in a
trace. We propose an information theoretically optimal privacy preserving
location release mechanism that takes temporal correlations into account. We
measure the privacy leakage by the mutual information between the user's true
and released location traces. To tackle the history-dependent mutual
information minimization, we reformulate the problem as a Markov decision
process (MDP), and solve it using asynchronous actor-critic deep reinforcement
learning (RL).
