Achieving privacy protection for crowdsourcing application in edge-assistant vehicular networking

Abstract

Crowdsourcing application, deemed as a key evolution on the way to vehicular networking, has great potential to provide real-time services. However, existing cloud-based vehicular networking cannot support real-time data transmission with wasting massive bandwidth resources. This paper studies the crowdsourcing application in edge-assistant vehicular networking. To improve the real-time demand of data transmission, we propose the E-node of that owns the learning and semantic analysis abilities. Then we analyze two data transmission scenarios of crowdsourcing for collected data: road map uploading, traffic accident and traffic flow. On the other hand, to address the privacy leakages in the process of data aggregation and data distribution, we separately design time-tolerance anonymous privacy protection algorithm and k − 1 location-offset privacy protection algorithm. Finally, we conduct extensive experiments to verify the effectiveness of our proposed privacy protection algorithms, including time delay, offset probability, privacy leakage probability and accuracy.