Transportation systems are growing and evolving towards more complex systems to provide increased security, safety, convenience, and efficiency for the escalating demands of society. The mortality rate of traffic accidents has increased to globally 1.35 million a year . These problems even becoming more critical due to the regular growing number of vehicles on the road and population increase in urban areas . Transport systems bring a number of recognised challenges such as traffic congestion, longer travelling time, higher CO 2 emission and fuel consumption, and accidents that may cause injuries and losses. The novelty of this work relies on translating the vehicular geospatial data to the graph-structured data for its higher applicability on the road network, designing a real-time application, and empirical analysis of privacy-efficacy optimality. Rather than sharing perturbed location on two-dimensional space, we propose a graph-based differential privacy solution for sharing location. In addition, data utility bounds are theoretically analysed, and vehicle routing efficacy is empirically analysed to evaluate the impact of the proposed scheme. positioning) sharing scheme is developed for edge cloud-assisted vehicles. Motivated by this, a privacy-preserving vehicular location (e.g. Despite offering clear advantages (such as overcoming congestion, saving fuel/energy/time, and reducing C O 2 emission), privacy concerns emerge due to the use of location data. Such systems usually rely on real-time traffic information that includes the temporal location feed of a vehicle. One of the major application areas is the Internet of Vehicles which can enhance existing capabilities, such as efficient vehicle route planning.

Internet of the Things (IoT) is being integrated into applications that are continuing to reshape many elements of our daily life.

Internet networx scheme