Differential Privacy Trajectory Protection Model Based on Personalized Spatiotemporal Clustering
With the proliferation of location-aware devices,trajectory data has found widespread applications in real-life scenarios.However,trajectory data is often associated with sensitive labels,and improperly sharing or disclosing such data can pose privacy threats to users,with varying levels of sensitivity among different datasets.To address this issue,a differential privacy trajectory protection model based on personalized spatiotemporal clustering was proposed.Firstly,in response to the vast amount of temporal data in trajectories and the need for privacy protection,the fuzzy clustering means algorithm(FCM)was proposed.Secondly,during the spatial segmentation process,clustering was performed based on density,and personalized adjustments were made to allocate privacy budgets,thereby enhancing data utility.In the trajectory synthesis phase,a comparison was made with real trajectory data to select trajectories that were more representative.Finally,the Laplace mechanism was introduced in the release phase to protect the privacy of trajectory counts.To validate the achievements of the model in terms of trajectory utility and privacy protection,comparisons were made with various models in four stages.The experimental results indicate a 15.45%improvement in data utility for the proposed model and,under the same privacy budget,enhances privacy protection strength by at least 35.62%.
万方数据
维普
