In a new paper published in Science Advances, Arnaud J. Tournier and Yves-Alexandre de Montjoye propose an entropy-based profiling attack for location data which shows that much more auxiliary information than previously believed is available to re-identify individuals in location data. The results show that individuals are correctly identified 79% of the time in a large location dataset of 0.5 million individuals. The proposed attack is robust to state-of-the-art noise addition and learns time-persistent profiles and their accuracy only slowly decreases over time (linear, roughly 1% per week).

Know more