Privacy-Preserving Deep Learning Model for Decentralized VANETs Using Fully Homomorphic Encryption and Blockchain

Abstract

In Vehicular Ad-hoc Networks (VANETs), privacy protection and data security during network transmission and data analysis have attracted attention. In this paper, we apply deep learning, blockchain, and fully homomorphic encryption (FHE) technologies in VANETs and propose a Decentralized Privacy-preserving Deep Learning (DPDL) model. We propose a Decentralized VANETs (DVANETs) architecture, where computing tasks are decomposed from centralized cloud services to edge computing (EC) nodes, thereby effectively reducing network communication overhead and congestion delay. We use blockchain to establish a secure and trusted data communication mechanism among vehicles, roadside units, and EC nodes. In addition, we propose a DPDL model to provide privacy-preserving data analysis for DVANET, where the FHE algorithm is used to encrypt the transportation data on each EC node and input it into the local DPDL models, thereby effectively protecting the privacy and credibility of vehicles. Moreover, we further use blockchain to provide a decentralized and trusted DPDL model update mechanism, where the parameters of each local DPDL model are stored in the blockchain for sharing with other distributed models. In this way, all distributed models can update their models in a credible and asynchronous manner, avoiding possible threats and attacks. Extensive simulations are conducted to evaluate the effectiveness, practicality, and robustness of the proposed DVANET system and DPDL models.