PAU: Privacy Assessment method with Uncertainty consideration for cloud-based vehicular networks

Abstract

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.