基于标签传播的轨迹兴趣点挖掘及隐私保护

Trajectory Interest Points Mining Based on Label Propagation and Privacy Protection

随着全球定位系统和移动数据采集设备的普及,产生了大量的轨迹数据,挖掘轨迹数据中潜在信息具有重要的现实意义,但在挖掘过程中存在着隐私信息泄露的危险。因此,本文提出一种基于标签传播的轨迹兴趣点挖掘及数据隐私保护机制,该机制将原始轨迹数据集进行预处理之后,进行基于密度的初次聚类,再运用改进的标签传播算法进行再次聚类,此算法在挖掘过程融入轨迹数据的多维度信息,提高了数据的利用率和兴趣点的精确度。同时,该机制融入一种基于改进的指数机制的差分隐私保护算法,此算法可以有效地保护用户的隐私信息不被泄露。对比实验结果表明,本文提出的方法与现有方法相比,具有更好的性能优势,同时有效地解决了用户隐私信息泄露的问题。

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 multidimensional 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 effectively 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.