Adaptive trust and privacy management framework for vehicular networks

By allowing vehicles to communicate on the roads, vehicular network is becoming a potential solution to improve the traffic safety. Both trust management and privacy protection play critical roles in vehicular network but there needs to be a trade-off between them. Existing works only focus on each issue separately or have not provided a satisfactory solution to both issues. In this paper, we propose a secure and flexible framework for vehicles to manage both trust and privacy. First, we design ALRS - a secure linkability scheme to enable vehicles to recognize either identities or trust levels of other vehicles despite them updating the pseudonyms to protect privacy. The linkability information is kept confidential from external attackers and unauthorized internal nodes using encryption and private set intersection technique. Besides, the linkability can be revoked easily to prevent nodes from being traced by other internal nodes. Second, we design ATMS - a context-aware trust management scheme that allows nodes to evaluate the trustworthiness of received events by considering the entity reputations of the senders. Under the context of privacy, obtaining the entity trust value is challenging. To overcome the challenge, we utilize the linkability information in ALRS and design a decision tree that estimates the entity trust adaptively to the available linkability information. Simulation results demonstrate that our framework helps nodes to simultaneously make accurate decisions towards the data and preserve their privacy in a flexible way. (C) 2018 Elsevier Inc. All rights reserved.