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Reinforcement Learning-Based Sensitive semantic location Privacy Protection for VANETs

China Communications 2021年第6期18卷 244-260页

作者： Minghui Min Weihang Wang Liang Xiao Yilin Xiao Zhu Han Department of Information and Communication Engineering Xiamen UniversityXiamen 361005China School of Information and Control Engineering ChinaUniversity of Mining and TechnologyXuzhou 221116China The Affiliated Hospital of China University of Mining and Technology Xuzhou 221116China Department of Electrical and Computer Engineering University of HoustonHouston TX 77004USA Beijing Key Laboratory of Mobile Computing and Pervasive Device No.6 Kexueyuan South RoadBeijing 100190China

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.

关键词： semantic location sensitivity locationbased services VANET differential privacy reinforcement learning

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location Privacy-Preserving Method Based on Degree of semantic Distribution Similarity

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国际计算机前沿大会会议论文集 2020年第1期 118-129页

作者： Rui Liu Kaizhong Zuo Yonglu Wang Jun Zhao College of Computer and Information Anhui Normal UniversityWuhu 241002AnhuiChina Anhui Provincial Key Laboratory of Network and Information Security Anhui Normal UniversityWuhu 241002AnhuiChina

While enjoying the convenience brought by location-based services,mobile users also face the risk of leakage of location ***,it is necessary to protect location *** existing privacy-preserving methods are based on K-anonymous and L-segment diversity to construct an anonymous set,but lack consideration of the distribution of semantic location on the road ***,the number of various semantic location types in the anonymous set varies greatly,which leads to semantic inference attack and privacy *** solve this problem,a privacy-preserving method is proposed based on degree of semantic distribution similarity on the road segment,ensuring the privacy of the anonymous ***,the feasibility and effectiveness of the method are proved by extensive experiments evaluations based on dataset of real road network.

关键词： location-based services Road network semantic location Privacy-preserving

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