مقاله انگلیسی رایگان در مورد ارزیابی حریم خصوصی برای شبکه های نقلیه ای مبتنی بر ابر – الزویر 2019

With the rapid progress of wireless communication and big data, the traditional Vehicular Ad-hoc Networks (VANETs) gradually evolve into the new Heterogeneous Vehicular Networks (HetVNets). Meanwhile, with the combination of multiple forms of communication modes, it initiates the Vehicle to Everything(V2X) communication model providing more efficient services. V2X communication generates much more private data than traditional VANETs, but the concerns over privacy breaches are increasing. these big data burdens the concerns about. To protect the privacy in these cloud-based vehicular networks is remained unsolved. In this paper, we propose Privacy Assessment method with Uncertainty consideration (PAU) to estimate the nodes’ capability in protecting privacy, and then choose the vehicular nodes with high priority calculated by PAU to improve the whole network’s privacy protection level. PAU expands subjective logic based on two-tuple to triad and keeps uncertainty as a constituent element. It evaluates the nodes by using the historical data from the vehicular cloud and the real-time data from V2V communications. The experiments and analysis show that the improvement of privacy-preserving capability achieved when applied PAU in Mix-zone scenarios.

Introduction

Vehicular Ad-hoc Networks(VANETs) are envisaged to be one of the building blocks of the Internet of cognitive Things and accelerate the evolution of the Intelligent Transportation System(ITS). Based on Americans 5G white paper[1], vehicle-to-everything(V2X) communication model is mainly composed by Vehicleto-Vehicle(V2V), Vehicle-to-Infrastructure (V2I), Vehicle-to-Network(V2N) and Vehicle-to-Pedestrian(V2P). The heterogeneous mode [2] accelerates the efficiency of information dissemination. However, it adds the concerns about privacy breaches. The long-term storage of historical data on the cloud platform adds to the worries about privacy issues. The heterogeneous vehicular networks increase the difficulties of privacy protection . There are three main dimensions taken into account in traditional entropybased privacy assessment methods, the specific aspects or types of privacy, the adversary and capabilities, and the privacy metric[3][4][5]. Those assessment methods are all considered to be off-line, which are quantitatively evaluated based on specific information or privacy breaches. In the cloud-based V2X network environment, on the one hand, it is challenging to evaluate every event with the high-speed of information dissemination, on the other hand, the results of the offline evaluation couldn’t make up for the data leakage. In the information interaction, a node’s low awareness of privacy protection will lessen the privacy protection capability of the entire communication system. To track this problem, we propose the Privacy Assessment method with Uncertainty consideration (PAU) metric based on vehicular nodes uncertainty assessment.