A Privacy-Preserving Prediction Method for Human Travel Routes
Wen-Chen Hu, Naima Kaabouch, Hung-Jen Yang
This paper proposes a kind of location-based research, human travel route prediction, which is to predict the track of a subject’s future movements. The proposed method works as follows. The mobile user sends his/her current route along with several dummy routes to the server by using a 3D route matrix, which encodes a set of routes. The server restores the routes from the 3D matrix and matches the restored routes to the saved routes. The predicted route is found as the trunk of the tree, which is built by superimposing the matching results. The server then sends the predicted routes back to the user, who will apply the predicted route to a real-world problem such as traffic control and planning. Preliminary experimental results show the proposed method successfully predicts human travel routes based on current and previous routes. User privacy is also rigorously protected by using a simple method of dummy routes.