AnonymousTollPass: A Blockchain-Based Privacy-Preserving Electronic Toll Payment Model

Abstract

As big data, Artificial Intelligence, and Vehicle-to-Everything (V2X) communication have advanced, Intelligent Transportation Systems (ITS) are being developed to enable efficient and safe transportation systems. Electronic Toll Collection (ETC), which is one of the services included in ITS systems, is an automated system that allows vehicles to pass through toll plazas without stopping for manual payment. The ETC system is widely deployed on highways due to its contribution to stabilizing the overall traffic system flow. To ensure secure and efficient toll payments, designing a distributed model for sharing toll payment information among untrusted toll service providers is necessary. However, the current ETC system operates under a centralized model. Additionally, both toll service providers and toll plazas know the toll usage history of vehicles. It raises concerns about revealing the entire driving routes and patterns of vehicles. To address these issues, blockchain technology, suitable for secure data management and data sharing in distributed systems, is being applied to the ETC system. Blockchain enables efficient and transparent management of ETC information. Nevertheless, the public nature of blockchain poses a challenge where users’ usage records are exposed to all participants. To tackle this, we propose a blockchain-based toll ticket model named AnonymousTollPass that considers the privacy of vehicles. The proposed model utilizes traceable ring signatures to provide unlinkability between tickets used by a vehicle and prevent the identity of the vehicle using the ticket from being identified among the ring members for the ticket. Furthermore, malicious vehicles’ identities can be traced when they attempt to reuse tickets. By conducting simulations, we show the effectiveness of the proposed model and demonstrate that gas fees required for executing the proposed smart contracts are only 10% (when the ring size is 50) of the fees required in previous studies. © 2024 Tech Science Press. All rights reserved.