Trajectory Differential Privacy Protection with Regional Center of Gravity
Yu Qiao, Hao Ji · 2022
In the process of location-based services, privacy preserving is a key challenge when releasing and analyzing data. Most of the existing methods added noise to all the locations in the trajectory, which will reduce the availability after data protection. Aiming at the data availability of trajectory privacy protection, a method about trajectory data differential privacy protection with regional center of gravity is proposed. Firstly, the position points in the trajectory are sampled, and the phased activity area is excavated to form an irregular polygon; Then, calculates the center of gravity (CG) of polygon and generates a new trajectory by using the CG points; After that, Laplace mechanism noise is added to the new trajectory to achieve the purpose of trajectory privacy protection. The experiment conducted on the real datasets shows that the proposed method can protect the privacy of trajectory data and further improve the availability of data.