MPC paper accepted!

In this paper, named Application of a predictive method to protect privacy of mobility data, we use the FLI technique to predict where users are going, to forecast whether there will be privacy issues in the near future, and reacting accordingly by enabling protections before said privacy issues occur.

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.

You can read the publication on ScienceDirect.