Game-theory based anonymous secure mutual authentication protocol with privacy-preserving for multi-level users in driverless taxi services

Abstract
In the field of Internet of Vehicles, driverless taxi (DT) services require secure and low-latency interactions among users, edge servers, DTs, and a trusted authority (TA). Nevertheless, existing vehicular authentication schemes are mainly designed for V2V, V2I, or multi-server environments and fail to account for variations in user behavior, interaction frequency, and risk level, thereby reducing service efficiency. To address these challenges, this paper proposes a game-theory based trust-adaptive Anonymous Secure Mutual Authentication (ASMA) protocol for multi-level users in DT services. ASMA integrates extended Chebyshev chaotic map operations into the authentication process to realize anonymous mutual authentication and session key negotiation. In addition, a dynamic trust evaluation mechanism is introduced to update user trust scores according to historical behaviors and current authentication outcomes, so that users are classified into premium, normal, and risky levels and are assigned differentiated authentication processes. In this way, premium users can complete authentication with lower latency, while risky users are required to undergo stricter verification. Security analysis in the Real-or-Random model and Scyther tool demonstrate that ASMA achieves its intended security goals. Performance evaluation on the NS-3 platform shows that ASMA provides favorable computational latency, communication overhead, and energy consumption compared with related schemes. A Nash equilibrium analysis experiment was conducted on the proposed hybrid game model, proving that the differentiated authentication strategy is an endogenous result under long-term incentives. At the same time, a dynamic convergence simulation of trust scores verifies that the evaluation mechanism can make the trust score converge stably to a reasonable range.