Game-Theoretic Framework for Private Data Sharing in Vehicular Networks

2026-06-20

AI Summary

This study addresses the dual challenges of privacy leakage and insufficient participation incentives in decentralized vehicular data sharing by proposing a hybrid framework that integrates Stackelberg game theory with secure multi-party computation. The proposed mechanism introduces, for the first time, a Stackelberg competition model into privacy-preserving vehicular data trading, dynamically balancing users’ privacy risks against economic incentives. By providing data consumers only with aggregated results rather than raw data, the approach effectively prevents exposure of original user information. Experimental evaluation using real-world vehicle trajectory data demonstrates that the scheme significantly reduces the success rate of trajectory reconstruction attacks while sustaining high user participation rates, thereby validating the feasibility of simultaneously achieving robust privacy protection and an active data marketplace.

Abstract

We present a novel game-theoretic framework designed to enhance privacy and scalability in decentralized vehicular data collection systems. The proposed hybrid architecture comprises vehicles that supply sensor data, independent servers that process data via secure multiparty computation, a coordinator node that manages data flow, and data consumers that set economic incentives. Crucially, our framework ensures that only the data consumer can access the fully aggregated data, preventing individual raw data exposure and significantly reducing privacy risks. By integrating principles of the Stackelberg competition from game theory, our approach dynamically balances privacy and economic incentives, enabling vehicles to make participation decisions based on perceived privacy risks and incentives. We empirically validate our framework using real-world vehicular location data, quantifying privacy risks by evaluating the accuracy with which a potential adversary can reconstruct a vehicle's path using only a subset of the shared data. This paper details the development and deployment of a data-trading platform within this framework, introducing a practical and privacy-preserving marketplace for profitable vehicle data sharing. Through experiments and simulations, we evaluate the effectiveness of the system in preserving privacy and explore the dynamics that influence vehicle participation. Our findings highlight the robustness of the proposed framework in preserving privacy while supporting an active data market.

Problem

• privacy
• vehicular networks
• data sharing
• game theory
• incentives

Innovation

• game-theoretic framework
• privacy-preserving data sharing
• secure multiparty computation
• Stackelberg competition
• vehicular networks