DLPM: A dynamic location protection mechanism supporting continuous queries

Currently, the protection of users' location privacy, particularly for moveable users, is a major concern for both academia and business. In order to receive required services, a moveable user needs to constantly disclose his/her location information with an untrusted third party in his/her locations, which raises security and privacy issues. To settle the above problems, in this work, we creatively integrate local differential privacy (LDP) with conditional random field (CRF) to facilitate continuous location sharing among moveable users. Firstly, we advance a novel approach of employing CRF to represent users' mobility. After that, we establish a system to provide continuous location sharing by combining the delta$$ \delta $$-location set and epsilon$$ \varepsilon $$-LDP. Finally, we evaluate the system performance on actual data sets. The experimental results indicate that our technique outperforms the planar isotropic mechanism (PIM) and AGENT.