A location privacy protection scheme based on characteristic trajectory points
Existing location privacy protection schemes often process all points on a trajectory,resulting in low data pro-cessing efficiency and poor usability.To address these issues,we propose a location privacy protection scheme based on characteristic trajectory points.This approach combines the optimized minimum description length with Dijkstra's algorithm to select representative characteristic trajectory points,achieving optimal trajectory segmentation and generating similar traj-ectories.We employ an optimized DBSCAN density clustering algorithm to process these similar trajectories,resulting in e-quivalence classes.Under the constraint of privacy budget,we select confusion points from the equivalence classes that have the smallest Fréchet distance to the user's actual characteristic trajectory points.These points are then connected in a time series to form confusion trajectories.By protecting characteristic trajectory points,the proposed scheme reduces the compu-tational complexity of the data and ensures data usability by selecting confusion points closest to the user's actual location.Comparative analysis with the IFTS and TP-MALS schemes shows that the proposed scheme enhances data processing effi-ciency while ensuring data usability.
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