Privacy preserving algorithm based on trajectory location and shape similarity

Abstract

In order to reduce the privacy disclosure risks when trajectory data is released,a variety of trajectories anonymity methods were proposed.However,while calculating similarity of trajectories,the existing methods ignore the impact that the shape factor of trajectory has on similarity of trajectories,and therefore the produced set of trajectory anonymity has a lower utility.To solve this problem,a trajectory similarity measure model was presented,considered not only the time and space elements of the trajectory,but also the shape factor of trajectory.It is computable in polynomial time,and can calculate the distance of trajectories not defined over the same time span.On this basis,a greedy clustering and data mask based trajectory anonymization algorithm was presented,which maximized the trajectory similarity in the clusters,and formed data ＂mask＂ which is formed by fully accurate true original locations information to meet the trajectory k-anonymity.Finally,experimental results on a synthetic data set and a real-life data set were presented; our method offer better utility and cost less time than comparable previous proposals in the literature.

Keywords

• spatio-tempporal trajectory data;publication of trajectory data;greedy clustering;data mask