A Hybrid Privacy-Preserving Framework for Cryptographic Efficiency in Vehicular Fog Computing Networks

Several data privacy issues are caused by the growing prevalence of smart cities, particularly when it comes to real-time vehicular networks. Vehicular fog computing has become a useful foundation for enabling decentralized, low-latency, and scalable data processing in such settings. However, privacy violations may arise from the sharing of private information in car networks. In this study, we present a homomorphic encryption (HE)-based privacy-preserving optimization technique for vehicular fog networks in smart cities. Without sacrificing performance, our method enables safe computing by encrypting data prior to processing. Using a simulated smart city setting, we verify the feasibility of our system and demonstrate that the suggested approach performs better than current approaches in terms of processing overhead and privacy protections. By offering a fair compromise between privacy concerns and computing efficiency, this method creates new opportunities for privacy-preserving applications in urban infrastructures.