A novel matrix representation for privacy-preserving spatial trajectory prediction

Abstract

Location-based services (LBS), one of mobile applications, have attracted a great attention recently. This research proposes a location-based service, which predicts a spatial trajectory based on the current and previous trajectories by using a novel matrix representation. Spatial trajectory prediction can be used in a variety of purposes such as travel recommendations and traffic control and planning, but at the same time, just like most location-based services, the user privacy concern is a major issue. Without rigorous privacy protection, users would be reluctant to use the service. The proposed method is simple but effective and user privacy is rigorously preserved at the same time because the trajectory prediction is performed at the user-side. Additionally, this research is not only useful but also pedagogical because it involves a variety of topics like (i) mobile computing, (ii) mobile security, and (iii) human behavior recognition.

Enabling 60 GHz Seamless Coverage for Mobile Devices: A Motion Learning Approach

“…They modify the algorithm into a new self-adaptive parameter selection algorithm called HMTP*, which is able to adjust parameters automatically according to the real world scene. Hu et al [10] try to predict the trajectory with the matrix representation method. The current and previous trajectories are represented as the matrix.…”