Biblio
In this paper, we extend the existing classification of signature models by Cao. To do so, we present a new signature classification framework and migrate the original classification to build an easily extendable faceted signature classification. We propose 20 new properties, 7 property families, and 1 signature classification type. With our classification, theoretically, up to 11 541 420 signature classes can be built, which should cover almost all existing signature schemes.
Vehicle ad-hoc network (VANET) is the main driving force to alleviate traffic congestion and accelerate the construction of intelligent transportation. However, the rapid growth of the number of vehicles makes the construction of the safety system of the vehicle network facing multiple tests. This paper proposes an identity-based aggregate signature scheme to protect the privacy of vehicle identity, receive messages in time and authenticate quickly in VANET. The scheme uses aggregate signature algorithm to aggregate the signatures of multiple users into one signature, and joins the idea of batch authentication to complete the authentication of multiple vehicular units, thereby improving the verification efficiency. In addition, the pseudoidentity of vehicles is used to achieve the purpose of vehicle anonymity and privacy protection. Finally, the secure storage of message signatures is effectively realized by using reliable cloud storage technology. Compared with similar schemes, this paper improves authentication efficiency while ensuring security, and has lower storage overhead.