A Privacy-Preserving Fog Computing Framework for Vehicular Crowdsensing Networks - Jiannan Wei (IEEE Access, 2018)
Recently, the study of road surface condition monitoring has drawn great attention to improve the traffic efficiency and road safety. As a matter of fact, this activity plays a critical role in the management of the transportation infrastructure. Trustworthiness and individual privacy affect the practical deployment of the vehicular crowdsensing network. Mobile sensing as well as the contemporary applications are made use of problem solving. The fog computing paradigm is introduced to meet specific requirements, including the mobility support, low latency, and location awareness. The fog-based vehicular crowdsensing network is an emerging transportation management infrastructure. Moreover, the fog computing is effective to reduce the latency and improve the quality of service. Most of the existing authentication protocols cannot help the drivers to judge a message when the authentication on the message is anonymous. In this paper, a fog-based privacy-preserving scheme is proposed to enhance the security of the vehicular crowdsensing network. Our scheme is secure with the security properties, including non-deniability, mutual authentication, integrity, forward privacy, and strong anonymity. We further analyze the designed scheme, which can not only guarantee the security requirements but also achieve higher efficiency with regards to computation and communication compared with the existing schemes.