Due to the distributed collaboration and privacy protection features, federated learning is a promising technology to perform the model training in virtual twins of Digital Twin for Mobile Networks (DTMN). In order to enhance the reliability of the model, it is always expected that the users involved in federated learning have trustworthy behaviors. Yet, available trust evaluation schemes for federated learning have the problems of considering simplex evaluation factor and using coarse-grained trust calculation method. In this paper, we propose a trust evaluation scheme for federated learning in DTMN, which takes direct trust evidence and recommended trust information into account. A user behavior model is designed based on multiple attributes to depict users’ behavior in a fine-grained manner. Furthermore, the trust calculation methods for local trust value and recommended trust value of a user are proposed using the data of user behavior model as trust evidence. Several experiments were conducted to verify the effectiveness of the proposed scheme. The results show that the proposed method is able to evaluate the trust levels of users with different behavior patterns accurately. Moreover, it performs better in resisting attacks from users that alternately execute good and bad behaviors compared with state-of-the-art scheme. The code for the method proposed in this paper is available at: https://web.xidian.edu.cn/jjguo/en/code.html .

Vehicular networks have huge potential to improve road safety and traffic efficiency, especially in the context of large models. Cloud computing can significantly improve the performance of vehicular networks, and the concept of cloud-assisted vehicular networks comes into being. Reputation management plays a crucial role in vehicular networks, since it can help each vehicle evaluate the trustworthiness of the other vehicles and the received messages. Reputation updating is essential in reputation management and it is usually done by the Trusted Authority (TA) regularly after collecting, decrypting, and verifying a large number of reputation feedbacks, which leads to great computation and communication overheads on the TA side and even makes the TA become the bottleneck of reputation management system. In this paper, we propose a novel Privacy-Preserving Reputation Updating (PPRU) scheme for cloud-assisted vehicular networks based on the Elliptic Curve Cryptography (ECC) and Paillier algorithms, in which the reputation feedbacks are collected and preprocessed by the honest-but-curious Cloud Service Provider (CSP) in a privacy-preserving manner, and the computation and communication overheads on the TA side can be dramatically reduced by about 88.36% and 83.88% as a result, respectively. Meanwhile, the proposed scheme can provide strong privacy preservation, strong security, and robust reputation management with acceptable computation and communication overheads. Furthermore, the comprehensive theoretical analysis and simulation evaluation are conducted, and the results demonstrate that the proposed scheme is significantly superior to the existing schemes in several aspects.

Vehicular networks have become a visible reality enabling information sharing between vehicles to enhance driving safety and provide value-added services to drivers and passengers. However, false information might be injected into the network because of defective sensors, malicious vehicles and so on. Therefore, an efficient mechanism to guarantee the reliability of information used by vehicles is of great important in vehicular networks. To solve this problem, this paper proposes a context awareness trust management model to evaluate the trustworthiness of messages received by vehicles to ensure bogus information will not influence the driving decision-making process. In the proposed scheme, the trust evaluation result of an evaluation request is determined by available related information and the evaluation strategy in current situation, which is unaffected by the presence of conflicting evidence and the trust level of entities in the network. Moreover, we design a reinforcement learning model that allows vehicles to adjust the evaluation strategy so as to maintain an accurate evaluation result in different driving scenarios. Extensive experiments were conducted in different driving scenarios to verify the effectiveness of the proposed model. The results show that our model is adaptive to different driving scenarios with negligible time overhead, regardless of the proportion of malicious nodes in the network. Furthermore, compared with three types of state-of-the-art trust models in different scenarios, our scheme can achieve a higher evaluation precision rate with no more computational and communication overhead in nonrandom road condition.

While enjoying the convenience brought by Location Based Service (LBS), the location privacy of vehicles in VANET may be disclosed. Distributed k -anonymity, as one of the most popular privacy protection methods, fails to take the trustworthiness of participants into account, resulting in malicious tracing of vehicles, which further leads to the sensitive information leakage, and even the safety threat of personal property. To address this issue, we propose a blockchain enabled trust-based location privacy protection scheme in VANET. Specifically, by analyzing the different requirements of the request vehicle and the cooperative vehicle during the process of constructing the anonymous cloaking region, as well as combining the characteristics of these two roles, we devise the trust management method based on Dirichlet distribution , such that both the requester and the cooperator will only cooperate with the vehicles they trust. Moreover, by employing blockchain, we also proposed the data structure to record the trustworthiness of vehicles on publicly available blocks timely, so that any vehicle can access the historical trust information of counterparties whenever necessary. Finally, the construction process of anonymous cloaking region is presented. Security analysis and extensive experiments indicate that the proposal is resilient to various trust model attacks, it can effectively detect the malicious vehicles, and preserve the location privacy of vehicles in the anonymous cloaking region construction, while the required time delay is limited.

As an important means of obtaining information of marine situation, the marine monitoring system relying on UAV has been paid more and more attention by all countries in the world, and the demand for tasks is growing continually. In UAV ad hoc networks, routing protocols with immutable routing policies that lack flexibility are generally incapable of maintaining effective performance due to the complicated and rapidly changing environmental situation and application requirements. In this paper, we propose an intelligent clustering routing approach (ICRA) for UANETs. The ICRA is composed of three components: the clustering module, the clustering strategy adjustment module and the routing module. In the clustering process, each node needs to calculate its utility. In order to maintain high topology stability and long network lifetime in different network states, the reinforcement learning based clustering strategy adjustment module needs continuous learning the benefits brought by adopting different strategies to calculate the nodes utility in a specific network state. With the learned knowledge, clustering strategy adjustment module could determine the optimal clustering strategy according to the current network state. In the routing phase, the proposed scheme can reduce the end-to-end delay and improve the packet delivery rate by introducing inter-cluster forwarding nodes to forward messages among different clusters. Extensive experiments have been conducted to verify ICRA’s robustness and superiority over existing schemes. The results demonstrate that ICRA could achieve better performance than its state-of-the-art counterparts with regard to the clustering efficiency, topology stability, energy efficiency and quality of service.

Recently, federated learning has received widespread attention, which will promote the implementation of artificial intelligence technology in various fields. Privacy-preserving technologies are applied to users’ local models to protect users’ privacy. Such operations make the server not see the true model parameters of each user, which opens wider door for a malicious user to upload malicious parameters and make the training result converge to an ineffective model. To solve this problem, in this article, we propose a poisoning attack defense framework for horizontal federated learning systems called ADFL. Specifically, we design a proof generation method for users to generate proofs to verify whether it is malicious or not. An aggregation rule is also proposed to make sure the global model has a high accuracy. Several verification experiments were conducted and the results show that our method can detect malicious user effectively and ensure the global model has a high accuracy.