Application of a predictive method to protect privacy of mobility data

Users of geo-localized applications on mobile devices need protection to avoid threats to their privacy. Such protection should vary in time, to cope with the dynamical nature of mobility data. We present a method to protect the privacy of users of location-based services, based on Model Predictive Control techniques. We employ three different predictors for future movements: an exact predictor, which serves as the baseline for the best expected performance, and two additional predictors allowing for online implementation. One of these predictors assumes the user is moving in a way that minimizes privacy, while the other is a linear predictor. The method has been applied to two datasets, Privamov and Cabspotting, which contain mobility data collected from real users when using a mobile device. The method demonstrated an improvement in privacy compared to a state-of-the-art mechanism by approximately 12% increase for Privamov users and 5% for Cabspotting users, while maintaining the same level of utility. • A Model Predictive Method to protect users of location-based services. • Two datasets with real user movement data were used to evaluate the method. • To assess the method, three predictors of future user positions were utilized. • The method improves privacy values compared to the current state of the art.