Reinforcement Learning-Based Sensitive Semantic Location Privacy Protection for VANETs

作     者：Minghui Min Weihang Wang Liang Xiao Yilin Xiao Zhu Han Minghui Min;Weihang Wang;Liang Xiao;Yilin Xiao;Zhu Han

作者机构：Department of Information and Communication EngineeringXiamen UniversityXiamen 361005China School of Information and Control EngineeringChinaUniversity of Mining and TechnologyXuzhou 221116China The Affiliated Hospital of China University of Mining and TechnologyXuzhou 221116China Department of Electrical and Computer EngineeringUniversity of HoustonHouston TX 77004USA Beijing Key Laboratory of Mobile Computing and Pervasive DeviceNo.6 Kexueyuan South RoadBeijing 100190China 

出 版 物：《China Communications》 (中国通信（英文版）)

年 卷 期：2021年第18卷第6期

页      面：244-260页

核心收录：

学科分类：12[管理学] 08[工学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0839[工学-网络空间安全] 081104[工学-模式识别与智能系统] 082304[工学-载运工具运用工程] 080204[工学-车辆工程] 0835[工学-软件工程] 0802[工学-机械工程] 081201[工学-计算机系统结构] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 0823[工学-交通运输工程] 

基　　金：This work was supported in part by National Natural Science Foundation of China under Grant 61971366 and 61771474 and in part by the Fundamental Research Funds for the central universities No.20720200077 and in part by Major Science and Technology Innovation Projects of Shandong Province 2019JZZY020505 and Key R&D Projects of Xuzhou City KC18171 and in part by NSF EARS-1839818 CNS1717454 CNS-1731424 and CNS-1702850 

主　　题：semantic location sensitivity locationbased services VANET differential privacy reinforcement learning 

摘      要：Location-based services(LBS)in vehicular ad hoc networks(VANETs)must protect users’privacy and address the threat of the exposure of sensitive locations during LBS *** release not only their geographical but also semantic information of the visited places(e.g.,hospital).This sensitive information enables the inference attacker to exploit the users’preferences and life *** this paper we propose a reinforcement learning(RL)based sensitive semantic location privacy protection *** scheme uses the idea of differential privacy to randomize the released vehicle locations and adaptively selects the perturbation policy based on the sensitivity of the semantic location and the attack *** scheme enables a vehicle to optimize the perturbation policy in terms of the privacy and the quality of service(QoS)loss without being aware of the current inference attack model in a dynamic privacy protection *** solve the location protection problem with high-dimensional and continuous-valued perturbation policy variables,a deep deterministic policy gradientbased semantic location perturbation scheme(DSLP)is *** actor part is used to generate continuous privacy budget and perturbation angle,and the critic part is used to estimate the performance of the *** demonstrate the DSLP-based scheme outperforms the benchmark schemes,which increases the privacy,reduces the QoS loss,and increases the utility of the vehicle.