Reinforcement Learning-Based Sensitive Semantic Location Privacy Protection for VANETs

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 requests.Users 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 patterns.In this paper we propose a reinforcement learning(RL)based sensitive semantic location privacy protection scheme.This 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 history.This 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 process.To 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 developed.The actor part is used to generate continuous privacy budget and perturbation angle,and the critic part is used to estimate the performance of the policy.Simulations demonstrate the DSLP-based scheme outperforms the benchmark schemes,which increases the privacy,reduces the QoS loss,and increases the utility of the vehicle.

Development and implementation of a dynamic and 4D GIS based on semantic location model

In the big data era,robust solutions are obliged to be proposed to integrate and represent data from different formats and with different contents to assist the decision-making.Current cartographic and geographic information systems have limited capabilities for solving these problems.This paper describes an automatic and comprehensive system that conducts data fusion from all potentially related sources.In this system,a new Semantic Location Model(SemLM)is established to present the semantic concepts and location feature and demonstrate how locations are interrelated.In the SemLM,various types of location descriptors in different application scenarios can be analyzed and understood.Additionally,considering the challenges involved in data-intensive computation and visualization,this paper implements a Place-based Pan-Information System(P2S)as an innovative 4D system that dynamically associates and visualizes place-based information,using public security as the case study.

Location Privacy-Preserving Method Based on Degree of Semantic Distribution Similarity

While enjoying the convenience brought by location-based services,mobile users also face the risk of leakage of location privacy.Therefore,it is necessary to protect location privacy.Most 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 segments.Thus,the number of various semantic location types in the anonymous set varies greatly,which leads to semantic inference attack and privacy disclosure.To 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 set.Finally,the feasibility and effectiveness of the method are proved by extensive experiments evaluations based on dataset of real road network.

Privacy Protection Algorithm for the Internet of Vehicles Based on Local Differential Privacy and Game Model

In recent years,with the continuous advancement of the intelligent process of the Internet of Vehicles(IoV),the problem of privacy leakage in IoV has become increasingly prominent.The research on the privacy protection of the IoV has become the focus of the society.This paper analyzes the advantages and disadvantages of the existing location privacy protection system structure and algorithms,proposes a privacy protection system structure based on untrusted data collection server,and designs a vehicle location acquisition algorithm based on a local differential privacy and game model.The algorithm first meshes the road network space.Then,the dynamic game model is introduced into the game user location privacy protection model and the attacker location semantic inference model,thereby minimizing the possibility of exposing the regional semantic privacy of the k-location set while maximizing the availability of the service.On this basis,a statistical method is designed,which satisfies the local differential privacy of k-location sets and obtains unbiased estimation of traffic density in different regions.Finally,this paper verifies the algorithm based on the data set of mobile vehicles in Shanghai.The experimental results show that the algorithm can guarantee the user’s location privacy and location semantic privacy while satisfying the service quality requirements,and provide better privacy protection and service for the users of the IoV.