Vehicular Ad Hoc Networks (VANETs) constitute a fundamental layer of Intelligent Transportation Systems (ITS), providing low-latency communication among vehicles and infrastructure for safer and more coordinated mobility. Maintaining real-time responsiveness during data aggregation, attaining large-scale operability, and protecting user privacy are all difficult tasks. The Edge-Assisted Privacy-Preserving Data Aggregation Framework (PPDAF), presented in this study, combines secure multiparty computation, differential privacy, and lightweight cryptographic primitives in a hierarchical edge-fog architecture. Through experimental evaluation using NS-3 combined with SUMO mobility scenarios, PPDAF achieves low processing and transmission overhead while achieving strong privacy protection (0.95), scalability (0.9), and accuracy (0.87). The findings also show better resistance against inference and collusion risks, as well as better latency performance. Overall, PPDAF provides a scalable, effective, and robust framework suitable for autonomous vehicle coordination and modern traffic management.
