Privacy-Preserving Digital Twin for Vehicular Edge Computing Networks

Abstract

As an emerging technology, digital twin (DT) has great potential to address the challenges posed by the dynamics and complexity of vehicles in vehicular edge computing (VEC) networks. By mapping the VEC network to the virtual space, DT can monitor vehicles, road side units (RSUs), channels, and resource usage in real time, further bringing comprehensive and accurate network analysis to the VEC network. However, the real-world implement of DT-empowered VEC networks cannot avoid the collection of privacy-sensitive information of participants. An incentive mechanism is necessitated to identify the qualities of participants without prior information and incent them to participate in DT modeling, so as to realize the requirement of privacy preserving while improving the DT modeling efficiency. In this paper, We propose a combined multi-armed bandit-based auction (CMABA) incentive mechanism that can identify the quality of clients in the VEC network without revealing sensitive and private information, and achieve the optimal performance of the model under budget constraints. The simulation results show that this scheme can significantly incent high-quality clients to participate in DT modeling under the requirement of privacy preserving and the constraint of limited budget, and improve the accuracy of DT modeling.