In the Internet of Vehicles (IoVs), certificateless-based authentication has emerged as a promising approach to secure federated learning (FL) participation. However, existing certificateless-based authentication schemes still exhibit three notable limitations: (i) some schemes cannot provide security against public-key replacement (PKR) attacks and key-leakage threats; (ii) most existing schemes do not take realistic coalition attacks into account; (iii) while some schemes applicable to FL settings can offer relatively comprehensive security guarantees, they often rely on costly cryptographic operations. These limitations make it challenging for certificateless authentication frameworks to simultaneously ensure high security and low latency, particularly in resource-constrained FL deployments within IoV environments. To address these challenges, we propose FedAuth, a certificateless, lightweight, and privacy-enhanced authentication scheme tailored for FL in IoVs. First, FedAuth establishes a structured pseudonymization mechanism that enables vehicles to anonymously participate in the FL process while maintaining accountability. Afterwards, FedAuth ensures robust authentication security by cryptographically anchoring key generation to each vehicle’s pseudonym, enforcing strict key validation, and introducing hash-bound multi-layered signature verification that prevents signature recombination, thereby resisting PKR attacks, coalition attacks, and key-leakage threats. Second, FedAuth guarantees the authenticity and integrity of gradients through lightweight signature construction, which supports batch authentication while avoiding expensive operations such as bilinear pairings, thereby reducing latency. Security analysis shows that FedAuth preserves identity privacy and ensures model integrity in the face of adversarial conditions. Experimental results confirm that FedAuth significantly reduces computational overhead by at least 43.42% and saves not less than 19.8 KB of communication cost per 100 vehicles, outperforming state-of-the-art schemes in both efficiency and robustness.

Collusion attacks, where the server and malicious clients collaborate to bypass gradient source confirmation or tamper with aggregation results, cause a fundamental damage to the training process in federated learning (FL). However, existing verifiable FL frameworks typically adopt a split-verification model-clients can independently validate the correctness of aggregation results, while the server is responsible for confirming the legitimacy of gradient sources. Thus, the collusion attack has emerged as a critical and intractable vulnerability, as it completely collapses this split-verification model, thereby invalidating such verification mechanisms. To tackle this fundamental issue, we propose an innovative dual-verifiable FL framework. Specifically, by leveraging vector commitments, our scheme first integrates both gradient source confirmation and aggregation result verification into a unified framework. Based on this unified design, our scheme implements two targeted strategies to defend against collusion attacks. To prevent collusion-enabled gradient source spoofing, our scheme introduces a semi-trusted verification cluster in place of unreliable server-side validation and embeds an anonymized identity-check strategy to collaboratively confirm gradient source legitimacy. To counter collusion-driven manipulation of gradient aggregation results, our scheme customizes auxiliary verification proofs with a computational one-wayness for client-uploaded gradients. This renders it infeasible for adversaries to tamper with the aggregation result through reverse engineering. Under experiments and security analyses, our scheme achieves reliable dual-verification and robust resistance to collusion attacks. Moreover, it reduces computation and communication overhead by at least 40.83% and 50.47%, respectively, compared to state-of-the-art verifiable FL schemes.

In Vehicular Ad-hoc Networks (VANETs), federated learning (FL) enables collaborative training a global intelligent transportation model without sharing vehicles’ raw data. Achieving model convergence in VANETs requires multiple rounds of FL for aggregation and updates. To improve the efficiency of model convergence, researchers explore methods of multi-round aggregation. The latest work, Flamingo (S&P 2023), proposes a multi-round aggregation scheme based on a reusable key mechanism. Its application in VANETs with dynamic network structures enhances the efficiency of model training. However, this scheme has a defect: vehicles cannot verify the correctness of the aggregation result. Once a semi-trusted server returns incorrect aggregation results due to computational errors, it may reduce the model’s accuracy or even cause training failure. In this scheme, we propose a verifiable multi-round aggregation scheme for FL in VANETs (VMFL), enabling vehicles to verify the aggregation results. Firstly, a multi-round verification mechanism is designed to reduce the number of interactions for vehicles by using reusable keys. Additionally, we propose a lightweight proof scheme that allows vehicles to verify the results with minimal computation, reducing the computational overhead of the verification process. Finally, a security analysis of VMFL is performed to demonstrate its security in cases of vehicle dropout. We validated the efficiency of VMFL across different datasets through experiments, showing no significant increase in time overhead compared to Flamingo, and demonstrating that its verification time is reduced by about 90% compared to the state-of-the-art scheme, thereby demonstrating the scheme’s usability.

Internet of Vehicles (IoVs) is increasingly used as a medium to propagate critical information via establishing connections between entities such as vehicles and infrastructures. During message transmission, privacy-preserving authentication is considered the first line of defence against attackers and malicious information. To achieve a more secure and stable communication environment, ever-increasing numbers of blockchain-based authentication schemes are proposed. At first glance, existing approaches provide robust architectures and achieve transparent authentication. However, in these schemes, verifiers need to conduct real-time operations in the blockchain (e.g., querying certificates). To remedy this limit, we propose a privacy-preserving blockchain-based authentication protocol with global-updated commitment (PBAG). In PBAG, based on the issued certificates, a public global commitment is computed, and a unique evaluation proof is generated for each authorized vehicle. Instead of querying the blockchain in real-time, verifiers can independently authenticate vehicles using the global commitment that is pre-updated with the assistance of the blockchain. Moreover, our scheme proposes a dynamic update mechanism to ensure the freshness of the global commitment and evaluation proofs. Benefiting from the update mechanism, there will be an authentication failure for vehicles holding invalid certificates when using the latest global commitment, thus avoiding the time-consuming of checking the Certificate Revocation List (CRL). In terms of privacy protection, our scheme provides privacy properties such as anonymity and unlinkability. It allows anonymous authentication based on evaluation proofs and achieves traceability of identity in the event of a dispute. The simulation demonstrates that the average computation cost of verifying per message is 0.36ms under the batch-enabled mechanism, reducing by more than 63.7% compared with existing schemes.

Real-time and interactive traffic information sharing systems are crucial in the Internet of Vehicles (IoV) as they enable vehicles to make informed decisions, thereby improving the efficiency of intelligent transportation systems (ITS). Message authentication ensures the accuracy, integrity, and tamper-resistance of information in IoV. Existing schemes aim to achieve time-critical message authentication . However, these schemes are time-consuming and cannot meet the real-time requirements of IoV. Additionally, there are issues with latency in data synchronization and data redundancy when vehicles traverse different domains. We propose an efficient, distributed, and resistant-to-malicious-attacks authentication scheme based on the reputation mechanism. Our scheme supports batch verification, enabling fast authentication. By leveraging the decentralized and ledger-synchronized features of blockchain , our distributed scheme reduces data redundancy. We also employ a reputation mechanism to ensure reliable reports in IoVs. We experimentally confirm that our scheme outperforms EADA (59.73%), RCoM (76.35%), MLGSDT (63.36%), and TRAJ (82.08%). This approach provides a secure and reliable solution for report authentication.