Homomorphic encryption based privacy-aware intelligent forwarding mechanism for NDN-VANET

Machine learning has been widely used for intelligent forwarding strategy in
 Vehicular Ad-Hoc Networks (VANET). However, machine learning has serious
 security and privacy issues. BRFD is a smart Receiver Forwarding Decision
 solution based on Bayesian theory for Named Data Vehicular Ad-Hoc Networks
 (NDN-VANET). In BRFD, every vehicle that received an interest packet is
 required to make a forwarding decision according to the collected network
 status information. And then decides whether it will forward the received
 interest packet or not. Therefore, the privacy information of a vehicle can
 be revealed to other vehicles during information exchange of the network
 status. In this paper, a Privacy-Aware intelligent forwarding solution
 PABRFD is proposed by integrating Homomorphic Encryption (HE) into the
 improved BRFD. In PABRFD, a secure Bayesian classifier is used to resolve
 the security and privacy issues of information exchanged among vehicle
 nodes. We informally prove that this new scheme can satisfy security
 requirements and we implement our solution based on HE standard libraries
 CKKS and BFV. The experimental results show that PABRFD can satisfy our
 expected performance requirements.