基于标签传播的轨迹兴趣点挖掘及隐私保护OACSTPCD

With the popularization of global positioning systems and mobile data collection devices,a large amount of trajectory data has been generated.Mining potential information in trajectory data has important practical significance,but there is a risk of privacy information leakage during the mining process.Therefore,we propose a trajectory interest point mining and data privacy protection mechanism based on label propagation.This mechanism preprocesses the original trajectory dataset,performs density based initial clustering,and then uses an improved label propagation algorithm for clustering.This algorithm incorporates multi-dimensional information of trajectory data in the mining process,improving data utilization and accuracy of interest points.At the same time,a differential privacy protection algorithm based on an improved exponential mechanism is proposed,which can effec-tively protect users'privacy information from being leaked.The comparative experimental results show that the proposed method has better performance advantages compared to existing methods,and effectively solves the problem of user privacy information leakage.