Homomorphic Encryption Based Privacy-Aware Intelligent Forwarding Mechanism for NDN-VANET

Xian Guo, Baobao Wang, Yongbo Jiang, Di Zhang, Laicheng Cao

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.