Protecting the location privacy of mobile social media users

Unprecedented volumes of location-based information have been produced as a result of the widespread adoption of social network applications and GPS-enabled devices and sensors. Publication of such location data can provide valuable resources for researchers and government agencies in applications ranging from near real-time population-wide health monitoring to planning for future cities. However, such data hold personally identifying information, which gives rise to many privacy issues. There is thus a pressing need for ways to restrict this inherently identifying location-related information, however ideally we would like to preserve the utility of the data. Importantly, any such solution has to be scalable to large population-wide data scenarios. To tackle this, we introduce a novel differentially private hierarchical location sanitization (DPHLS) approach based on the concept '(α, r)-dataset' implemented through a Variable Order Mobility Markov Model (VO3M). We show how this system allows individual locations in personal trajectories to be protected using selection and frequency perturbation mechanisms using the '(α, r)-dataset', leveraging past (published) location histories to obfuscate the user location in a flexible and controllable manner. The effectiveness and efficiency of the proposed solution is evaluated through the big data experiments that have been carried out using an OpenStack-based Cloud and Apache Sparkbased platform utilising large-scale social media trajectories. The experimental results suggest that the privacy publication algorithm can successfully scale to big data scenarios whilst retaining the utility of the datasets (trajectories) and preserving individual user privacy.