The automotive industry is transforming from traditional private vehicle ownership to innovative shared mobility solutions, presenting unprecedented cybersecurity challenges. This transition introduces complex security vulnerabilities where malicious actors could exploit the access of a rental vehicle to manipulate the software systems on board. Unlike physical damage, which can be easily detected, software modifications represent an insidious threat that can compromise user safety and vehicle integrity. Our research proposes a blockchain-based approach to address these critical security challenges. We introduce a novel method for ensuring data authenticity and integrity within vehicle systems by leveraging blockchain’s immutable ledger and advanced encryption technologies. Our methodology utilizes the Trusted Platform Module (TPM) to securely archive vehicle data in the central gateway, creating a tamper-evident environment that fundamentally transforms traditional data management approaches. The key innovation lies in the blockchain-based data binding process: when a user possesses a vehicle, they bind application-retrieved data with the vehicle’s existing data and commit them to the blockchain. Upon vehicle return, any potential tampering can be immediately detected by comparing newly acquired data against pre-existing blockchain records. We develop a proof-of-concept implementation and demonstrate significant improvements in security architecture that offer a reliable alternative to conventional database-centric approaches. Comparative evaluations between database-centric and blockchain-centric architectures testify to the operational effectiveness and practical viability of our proposed solution. By addressing the inherent vulnerabilities in shared mobility ecosystems, this research contributes to a sophisticated technological intervention that enhances user safety, data integrity, and trust in emerging transportation paradigms.
The fulltext files of this resource are currently embargoed.Embargo end: 2026-06-27
Ghani, UroojAslam, Mudassar Ullah, SubhanAhmad, TahirBuriro, AttaullahIngle, Palash Yuvraj Jhaveri, Rutvij H.
School of Engineering, Computing and Mathematics
Year of publication: 2025Date of RADAR deposit: 2025-06-18
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