  In the context of evolving smart cities and autonomous transportation
systems, Vehicular Ad-hoc Networks (VANETs) and the Internet of Vehicles (IoV)
are growing in significance. Vehicles are becoming more than just a means of
transportation; they are collecting, processing, and transmitting massive
amounts of data to make driving safer and more convenient. However, this
advancement ushers in complex issues concerning the centralized structure of
traditional vehicular networks and the privacy and security concerns around
vehicular data. This paper offers a novel, game-theoretic network architecture
to address these challenges. Our approach decentralizes data collection through
distributed servers across the network, aggregating vehicular data into
spatio-temporal maps via secure multi-party computation (SMPC). This strategy
effectively reduces the chances of adversaries reconstructing a vehicle's
complete path, increasing privacy. We also introduce an economic model grounded
in game theory that incentivizes vehicle owners to participate in the network,
balancing the owners' privacy concerns with the monetary benefits of data
sharing. This model aims to maximize the data consumer's utility from the
gathered sensor data by determining the most suitable payment to participating
vehicles, the frequency in which these vehicles share their data, and the total
number of servers in the network. We explore the interdependencies among these
parameters and present our findings accordingly. To define meaningful utility
and loss functions for our study, we utilize a real dataset of vehicular
movement traces.
