A Cloud-Assisted Multi-Dimensional Reputation Management Scheme With Privacy Preservation for Emergency Message Dissemination in VANETs

The proliferation of Vehicular Ad-hoc Networks (VANETs) has brought new security challenges, as malicious vehicles may disseminate fake messages, thereby threatening road safety. Therefore, it is crucial to ensure the credibility of emergency messages while enabling their rapid dissemination. Cloud computing can enhance the efficiency of VANETs, thus facilitating the application of cloud-assisted security mechanisms. Therefore, this paper proposes a Cloud-assisted Multi-dimensional Reputation Management (CMRM) scheme with privacy preservation for emergency message dissemination in VANETs. Specifically, vehicles are represented by multi-dimensional reputation vectors together with dynamically updated multi-dimensional threshold vectors, which enable more fine-grained and reliable reputation evaluation. A confusing process is employed to conceal vehicle identities, reputation vectors, and threshold vectors, thereby ensuring privacy preservation. Furthermore, we design a Reputation Judgment Vector Extraction (RJVE) algorithm. This algorithm, through the collaboration of two cloud servers, can obtain the reputation judgment vector without disclosing sensitive data. Based on this, receivers use the Credible Message Judgment (CMJ) algorithm to efficiently evaluate the credibility of emergency messages. Theoretical analysis demonstrates that the CMRM scheme achieves privacy preservation and resists common attacks with low computation and communication overhead on both the Trusted Authority (TA) and vehicle sides. Simulation results further verify the robustness and efficiency of the CMRM scheme. The probability that a true message is trusted can quickly converge to over 80%, and the probability that a fake message is trusted quickly drops to near 0.