Providing an efficient anonymous authentication scheme in vehicular ad hoc networks (VANETs) with low computational cost is a challenging issue. Even though, there are some existing schemes to provide anonymous authentication, the existing schemes suffer from high computational cost in the certificate and the signature verification process, which leads to high message loss. Therefore, they fail to meet the necessity of verifying hundreds of messages per second in VANETs. In our scheme, we propose an efficient anonymous authentication scheme to avoid malicious vehicles entering into the VANET. In addition, the proposed scheme offers a conditional tracking mechanism to trace the vehicles or roadside units that abuse the VANET. As a result, our scheme revokes the privacy of misbehaving vehicles to provide conditional privacy in a computationally efficient way through which the VANET entities will be anonymous to each other until they are revoked from the VANET system. Moreover, the proposed scheme is implemented and the performance analysis shows that our scheme is computationally efficient with respect to the certificate and the signature verification process by keeping conditional privacy in VANETs.

In this work, the federated learning mechanism is introduced into the deep learning of medical models in Internet of Things (IoT)-based healthcare system. Cryptographic primitives, including masks and homomorphic encryption, are applied for further protecting local models, so as to prevent the adversary from inferring private medical data by various attacks such as model reconstruction attack or model inversion attack, etc. The qualities of the datasets owned by different participants are considered as the main factor for measuring the contribution rate of the local model to the global model in each training epoch, instead of the size of datasets commonly used in deep learning. A dropout-tolerable scheme is proposed in which the process of federated learning would not be terminated if the number of online clients is not less than a preset threshold. Through the analysis of the security, it shows that the proposed scheme satisfies data privacy. Computation cost and communication cost are also analyzed theoretically. Finally, skin lesion classification using training images provided by the HAM10000 medical dataset is set as an example of healthcare applications. Experimental results show that compared with existing schemes, the proposed scheme obtained promising results while ensuring privacy preserving.

Edge intelligent computing is widely used in the fields, such as the Internet of Medical Things (IoMT) and industrial control UAV clusters, which has advantages, including high data processing efficiency, strong real-time performance and low network delay. However, there are many problems including privacy disclosure, limited calculation force when edge intelligent devices, edge gateways and clouds complete the task unloading, as well as scheduling and coordination issues. Federated learning allows all training devices to complete training at the same time, which greatly improves training efficiency. However, traditional federated learning will expose patient's privacy information of the training set. Due to the sensitive nature of the healthcare data, the aforementioned approach of transferring the patient's data to the central servers may create serious security and privacy issues. Therefore, this article proposes a Privacy Protection Scheme for Federated Learning under Edge Computing (PPFLEC). First of all, we propose a lightweight privacy protection protocol based on a shared secret and weight mask, which is based on a random mask scheme of secret sharing. It is more accurate and efficient than federated learning for secure multiparty computing frameworks based on homomorphic encryption. It can not only protect gradient privacy without losing model accuracy, but also resist equipment dropping and collusion attacks between devices. Second, we design an algorithm based on a digital signature and hash function, which achieves the \textcolor{blue}{integrity and consistency} of the message, as well as resisting replay attacks. Finally, we propose a periodic average training strategy, compared with differential privacy to prove that our scheme is 40% faster in efficiency than in deferential privacy. Meanwhile, compared with federated learning, we can achieve the same efficiency under the condition of ensuring safety. Therefore, our scheme can work well in unstable edge computing environments such as smart healthcare.

Vehicular ad‐hoc networks (VANETs) are the most hopeful approach to provide safety information and other infotainment applications to both drivers and passengers. VANETs are formed by intelligent vehicles equipped with On Board Units and wireless communication devices. Hence, VANETs become a key component of the intelligent transport system. Even though VANETs are used in enormous number of applications, there are many security challenges and issues that need to be overcome to make VANETs usable in practice. A great deal of study has been done towards it, but security mechanisms in VANETs are not effective. This study provides a summary about the VANET, characteristics and security challenges. This study also provides a summary of some major security attacks on security services such as availability, confidentiality, authentication, integrity and non‐repudiation and the corresponding countermeasures to make VANET communications more secure.

Increasingly, governments around the world, particularly in technologically advanced countries, are exploring or implementing smart homes, or the related smart facilities for the benefits of the society. The capability to remotely access and control Internet of Things (IoT) devices (e.g. capturing of images, audios and other information) is convenient but risky, as vulnerable devices can be exploited to conduct surveillance or perform other nefarious activities on the users and organizations. This highlights the necessity of designing a secure and efficient remote user authentication solution. Most of the existing solutions for this problem are generally based on a single-server architecture, which has limitations in terms of privacy and anonymity (leading to users’ daily activities being predicted), and integrity and confidentiality (resulting in an unreliable behavior auditing). While blockchain-based solutions may mitigate these issues, they still face with some critical challenges (e.g. providing regulation of behaviors and privacy protection of access policy). Motivated from these facts, in this paper, we construct a novel secure mutual authentication system, which can be applied in smart homes and other applications. Specifically, the proposed approach integrates blockchain, group signature and message authentication code to provide reliable auditing of user’s access history, anonymously authenticate group members, and efficiently authenticate home gateway, respectively. We also prove the security and privacy requirements, including anonymity, traceability and confidentiality, that the proposed system satisfies, with an implementation and evaluation to demonstrate its practicality.

The rapid and convenient travel of people and the timely transportation of goods depend on the correct decision of the Intelligent Transportation Systems (ITS). Due to the decision-making of ITS requires a large amount of data to support, UAV-enabled periodic data collection is an effective method. However, due to the limited resources of UAV, UAV cannot directly collect data from all storage devices, resulting in unfair data collection. Therefore, we propose a UAV Speed Control based Fairness Data Collection (USCFDC) scheme. First, since the fairness of data collection will affect the decision-making of ITS, a framework for controlling the flight speed of the UAV is proposed to improve the fairness of data collection. The flight speed of UAV will slow down in areas with a large number of nodes, thereby improving the fairness of data collection. Second, a novel method is proposed to maximize the amount of data collected by UAV from each node. With this method, the value of the amount of data will be used as the dichotomous value in the dichotomy algorithm, and the UAV must collect a certain amount of data from each node. The upper and lower limits of the dichotomy algorithm are adjusted according to the time duration for UAV to collect data. Compared with previous schemes, the fairness of data collection can be improved by a maximum of 15.89% under the same flight time of UAV. Besides, the energy consumption is reduced by 49.31%-52.55% and the flight time of the UAV is reduced by 48%-62.38% when the amount of collected data is the same.