Vehicular Edge-Based Approach for Optimizing Urban Data Privacy

The rapid progress of the artificial intelligence (AI) sector has greatly impacted vehicular edge components (VECs) in the vehicular ad hoc network (VANET). Various AI applications, including automatic driving, preaccident alerts, and video broadcasting, have become essential to meet VANET’s diverse requirements. However, implementing these applications in the resource-constrained urban sensing environment poses challenges. To overcome this, we proposed a novel approach that partitions resource-intensive ciphertext-policy attribute-based decryption (CP-ABE) tasks based on ciphertext (CT) policy into sub-CTs using machine learning. Our technique, CT-distribution DE (CD-DE), utilizes differential evolution (DE) to decrypt CP-ABE tasks on VECs. It includes a selection algorithm that allows the data owner vehicle to choose VEC components for decryption operations. Compared to widely used techniques such as particle swarm optimization (PSO) and genetic algorithm (GA), CD-DE offers lower overhead and provides accurate near-optimal solutions across most scenarios. Our study demonstrates the effectiveness of CD-DE in improving the efficiency and accuracy of resource-intensive CP-ABE tasks in VANETs. The proposed approach addresses the challenges posed by limited resources, bandwidth, and workload constraints in the urban sensing environment. By enabling efficient implementation of AI-based applications in vehicles within urban environments, our approach holds promise for enhancing VANETs’ capabilities.