基于聚类和深度学习的车联网轨迹隐私保护机制  

Clustering and deep learning based trajectory privacy protection mechanism for Internet of vehicles

作　　者：申自浩[1] 唐雨雨 王辉[2] 刘沛骞 刘琨[2] SHEN Zihao;TANG Yuyu;WANG Hui;LIU Peiqian;LIU Kun(School of Computer Science and Technology,Henan Polytechnic University,Jiaozuo 454000,China;School of Software,Henan Polytechnic University,Jiaozuo 454000,China)

机构地区：[1]河南理工大学计算机科学与技术学院,河南焦作454000 [2]河南理工大学软件学院,河南焦作454000

出　　处：《浙江大学学报（工学版）》2024年第1期20-28,共9页Journal of Zhejiang University：Engineering Science

基　　金：国家自然科学基金资助项目(61300216);河南省高等学校重点科研资助项目(23A520033);河南理工大学博士基金资助项目(B2022-16,B2020-32)。

摘　　要：针对车联网轨迹发布中用户面临的隐私泄露问题,提出基于聚类和深度学习的轨迹隐私保护机制(PPCDL).考虑轨迹中的时间因素,通过时间戳将轨迹空间划分为多个区域,获取区域中的轨迹分布点.对每个区域进行改进稳定隶属度多峰值聚类,根据区域轨迹密度进行隐私预算矩阵的预分配.利用时间图卷积网络模型提取轨迹数据的时空特征,对隐私预算预分配矩阵进行训练和预测.根据预测结果添加相应的拉普拉斯噪声,在轨迹数据发布前进行扰动.理论分析和实验结果表明,PPCDL相较于对比机制,时间开销更少,能够更精确地预测隐私预算.利用PPCDL可以合理地在轨迹数据中添加拉普拉斯噪声,有效地提高了轨迹数据的可用性.A trajectory privacy protection mechanism based on clustering and deep learning(PPCDL)was proposed aiming at the problem of privacy leakage faced by users in the trajectory distribution of Internet of Vehicles.The trajectory space was divided into multiple regions using timestamps by considering the time factor in the trajectory in order to obtain the distribution points of trajectories within each region.Improved stable membership multi-peak clustering was performed on each region,and the privacy budget matrix was pre-allocated based on the trajectory density of each region.The time graph convolutional network model was utilized to extract spatiotemporal features from trajectory data for training and predicting the pre-allocated privacy budget matrix.The trajectory data was perturbed by adding the appropriate Laplace noise based on the prediction results before it was published.The theoretical analysis and experimental results show that PPCDL has lower time overhead and can predict the privacy budget more accurately compared with the comparison mechanism.Laplace noise can be added to the trajectory data in a reasonable manner by using PPCDL,which effectively improves the availability of the trajectory data.

关 键 词：隐私保护 密度峰值聚类 轨迹隐私 时间图卷积网络 隐私预算 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]