User Trajectory Identification Model via Attention Mechanism

Jisuanji kexue

LI Hao, CAO Shu-yu, CHEN Ya-qing, ZHANG Min¹ 

[1] Department of TCA,Institute of Software,Chinese Academy of Sciences,Beijing 100190,China;

关键词: trajectory privacy|trajectory-user identification|deep learning|recurrent neural network|attention mechanism;   

DOI  :  10.11896/jsjkx.210300231

来源: DOAJ

【 摘 要 】

Recently the application of location-based services has gradually become popular.It provides convenience in people's daily life,and also brings a great threat to personal privacy.The existing research shows that,with a large amount of historical trajectory data,attackers can identify the user who generates the trajectory from the anonymous trajectory dataset.In these rela-ted studies,both data sparsity and poor data quality are faced.Data sparsity refers to the fact that trajectories are often distributed only in a few local areas,and there is no large corpus contrast to the natural language processing field.The poor data quality refers to the low sampling rate and existing noise of the location points in a trajectory.To address these two problems,this paper proposes a user trajectory identification model based on attention mechanism,including the location embedding module,the attention-based transitional feature encoder and trajectory-user identification module.The location embedding module is used to embed the spatial relation of the trajectory points into the location vector;the attention-based transitional feature encoder is used to extract the sequential dependencies from a single trajectory;and the trajectory-user identification module is used to predict the user identity of the trajectory based on the outputs of the transitional feature encoder.Finally,the experimental verification is carried out on Gowalla and Geolife datasets.The experimental results show that the proposed model in this paper can effectively alleviate the problem of data sparsity and poor data quality,and can achieve better accuracy than existing methods.

【 授权许可】

Unknown