|Table of Contents|

Privacy-preserved Vehicular Trajectory Embedding Federated Learning and Clustering(PDF)

南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2022年02期
Page:
80-86
Research Field:
计算机科学与技术
Publishing date:

Info

Title:
Privacy-preserved Vehicular Trajectory Embedding Federated Learning and Clustering
Author(s):
Kong Xiuping1; Lu Lin2
(1.Department of Information Center,Yangzhou Polytechnic Institute,Yangzhou 225127,China)(2.China Electronic Cloud Digital Intelligence Technology Co.,Ltd.,Wuhan 430056,China)
Keywords:
sequential autoencoder; federated learning; differential privacy; trajectory clustering
PACS:
TP311.1
DOI:
10.3969/j.issn.1672-1292.2022.02.012
Abstract:
High dimensional trajectory data of intelligent networked vehicles are widely used to find different motion patterns from vehicle trajectories,so as to reduce traffic risk and improve traffic efficiency. However,more and more attention has been paid to the privacy problem in the process of data utilization. How to research and apply the algorithm under the premise of privacy protection is a big challenge. In view of the background that vehicle trajectory data are scattered among different owners and cannot be shared due to privacy protection,this paper uses differential privacy federated learning framework to construct a sequence autoencoding network to extracting low dimensional representation of trajectory sequence,and such latent representation is further used to find frequent vehicle routes in different periods. The proposed framework not only avoids the sharing of user privacy data through local training,but also prevents the disclosure of model information through Gaussian differential privacy mechanism. The framework is validated on real trajectory data sets,using LSTM autoencoder as embedding learning network,and compared with non-federated and non-differential encryption models. Finally,through clustering analysis,it is found that the learning model under the framework can still find effective frequent trajectories under the premise of fully respecting privacy protection.

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