Privacy-Preserving Federated Learning (PPFL) is an emerging secure distributed learning paradigm that aggregates user-trained local gradients into a federated model through a cryptographic protocol. Unfortunately, PPFL is vulnerable to model poisoning attacks launched by a Byzantine adversary, who crafts malicious local gradients to harm the accuracy of the federated model. To resist model poisoning attacks, existing defense strategies focus on identifying suspicious local gradients over plaintexts. However, the Byzantine adversary submits encrypted poisonous gradients to circumvent existing defense strategies in PPFL, resulting in encrypted model poisoning. To address the issue, in this paper we design a privacy-preserving defense strategy using two-trapdoor homomorphic encryption (referred to as ShieldFL), which can resist encrypted model poisoning without compromising privacy in PPFL. Specially, we first present the secure cosine similarity method aiming to measure the distance between two encrypted gradients. Then, we propose the Byzantine-tolerance aggregation using cosine similarity, which can achieve robustness for both Independently Identically Distribution (IID) and non-IID data. Extensive evaluations on three benchmark datasets ( i.e., MNIST, KDDCup99, and Amazon) show that ShieldFL outperforms existing defense strategies. Especially, ShieldFL can achieve 30%-80% accuracy improvement to defend two state-of-the-art model poisoning attacks in both non-IID and IID settings.

Autonomous vehicles (AVs) are increasingly common, although there remain a number of limitations that need to be addressed in order for their deployment to be more widespread. For example, to mitigate the failure of self-driving functions in AVs, introducing the remote control capability (which allows a human driver to operate the vehicle remotely in certain circumferences) is one of several countermeasures proposed. However, the remote control capability breaks the isolation of onboard driving systems and can be potentially exploited by malicious actors to take over control of the AVs; thus, risking the safety of the passengers and pedestrians (e.g., AVs are remotely taken over by terrorist groups to carry out coordinated attacks in places of mass gatherings). Therefore, security is a key, mandatory feature in the design of AVs. In this paper, we propose a cloud-centric three-factor authentication and key agreement protocol (CT-AKA) integrating passwords, biometrics and smart cards to ensure secure access to both cloud and AVs. Three typical biometric encryption approaches, including fuzzy vault, fuzzy commitment, and fuzzy extractor, are unified to achieve three-factor authentication without leaking the biometric privacy of users. Moreover, two session keys are negotiated in our protocol, namely: one between the user and AV to support secure remote control of the AV, and the other is negotiated between the mobile device and the cloud to introduce resilience to the compromise of ephemeral security parameters to ensure cloud data access security with a high security guarantee. Finally, we formally verify the security properties and evaluate the efficiency of CT-AKA, whose findings demonstrate that the protocol achieves high security strength with reasonable computation and communication costs.

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.

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.

With the assistance of the Internet of Things, the fast developing Healthcare Internet of Things (H-IoT) has promoted the healthcare ecosystem into the era of Health 5.0 and enables many promising medical applications, such as remote healthcare that is crucial in pandemic (e.g., coronavirus disease 2019). Healthcare participants can make accurate diagnosis, treatment, and research based on the shared personal health records (PHRs) sensed from remote H-IoT devices. However, current H-IoT systems fall short of a secure and trustworthy PHR sharing service in remote healthcare, which is able to prevent user privacy leakage and PHR integrity violation together with high efficiency in key distribution alongside efficient data retrieval and fine-grained access control. In response, we present a blockchain-based hierarchical data sharing framework (BHDSF) to provide fine-grained access control and efficient retrieval over encrypted PHRs with low consumed hierarchical key distribution and key leakage resistance. Compared with the existing solutions, the BHDSF takes both untrusted cloud and malicious auditor into consideration simultaneously and achieves trustworthy PHR integrity auditing and metadata verification by leveraging the blockchain technique. Besides, the BHDSF enables efficiently aggregative authentication for the trustworthiness of source records from H-IoT devices, which is lacked in most of the existing data sharing frameworks. Finally, we demonstrate the feasibility of the BHDSF by conducting extensive empirical tests over a real-world dataset.