Agility maneuvers to mitigate inference attacks on sensed location data

Sensed location data is subject to inference attacks by cybercriminals that aim to obtain the exact position of sensitive locations, such as the victim's home and work locations, to launch a variety of different attacks. Various Location-Privacy Preserving Mechanisms (LPPMs) exist to reduce the probability of success of inference attacks on location data. However, such mechanisms have been shown to be less effective when the adversary is informed of the protection mechanism adopted, also known as white-box attacks. We propose a novel approach that makes use of targeted agility maneuvers as a more robust defense against white-box attacks. Agility maneuvers are systematically activated in response to specific system events to rapidly and continuously control the rate of change in system configurations and increase diversity in the space of readings, which would decrease the probability of success of inference attacks by an adversary. Experimental results, performed on a real data set, show that the adoption of agility maneuvers reduces the probability of success of white-box attacks to 2.68% on average, compared to 56.92% when using state-of-the-art LPPMs.