Currently, the popularity of the Internet of Things (IoT) has brought about an increase in the amount of data, so multi-server distributed cloud computing has been widely used in various applications that have brought convenience to our daily lives. At the same time, the development of the fifth generation (5G) of mobile communication technology has gradually become the main driving force for the popularization of the IoT. Because the 5G network is a heterogeneous network with multiple servers and small cells, the mutual authentication protocol under multiple servers is also applicable to the 5G network environment. However, much of the data will have serious storage and security issues during transmission. Aiming at the security issues in a multi-server (M-S) architecture, in 2018, Wu et al. proposed an authentication protocol in a distributed cloud environment. They claimed that their protocol is secure and resistant to various known types of attacks. However, we found that their protocol does not guarantee perfect forward secrecy (PFS) and suffers from privileged insider (PI) attacks. Such attacks will cause data to be out of sync. Therefore, we improved Wu et al.’s protocol and proposed an improvement in the 5G network environment. Finally, we performed a security analysis on the proposed protocol, including the automatic encryption protocol tool ProVerif, BAN logic, and informal security analysis, which proved that our protocol is secure. Compared with similar existing schemes, we have proved the efficiency of the scheme and achieved higher security standards.

In recent years, Internet of Things (IoT)-enabled health monitoring wearable devices have become a trend in healthcare systems, regularly collecting vital sign data from patients and uploading them to the cloud. Through on-demand search queries, data are shared with third-party healthcare service providers to monitor patients' health status and provide timely diagnoses. To ensure privacy and security, patient health data should be encrypted before being uploaded to the cloud. The cloud can give search encryption services. However, current searchable encryption (SE) technologies still have problems with forward privacy security and verifiability. This article proposes an IoT-cloud-enabled healthcare data system incorporating a SE method with forward privacy and verifiability. By designing a trapdoor permutation function, we render the resulting output indistinguishable from meaningless random data to the adversary. Thus, the adversary cannot judge the relationship between a newly inserted record and a past search token, and therefore, the system realizes forward privacy or forward secrecy. We propose a multikeyword search verification mechanism based on a pseudo-random function. Our approach solves verifying the correctness of search results in the top-k search scenario with partial search results. A formal security analysis proves that our scheme achieves forward privacy preservation, which can help guarantee healthcare data privacy. Additionally, a performance evaluation shows that our method is efficient and effective, providing an information security system to preserve patient privacy in IoT-enabled healthcare systems.

With the intelligentization of Maritime Transportation System (MTS), Internet of Thing (IoT) and machine learning technologies have been widely used to achieve the intelligent control and routing planning for ships. As an important branch of machine learning, federated learning is the first choice to train an accurate joint model without sharing ships' data directly. However, there are still many unsolved challenges while using federated learning in IoT-enabled MTS, such as the privacy preservation and Byzantine attacks. To surmount the above challenges, a novel mechanism, namely DisBezant, is designed to achieve the secure and Byzantine-robust federated learning in IoT-enabled MTS. Specifically, a credibility-based mechanism is proposed to resist the Byzantine attack in non-iid (not independent and identically distributed) dataset which is usually gathered from heterogeneous ships. The credibility is introduced to measure the trustworthiness of uploaded knowledge from ships and is updated based on their shared information in each epoch. Then, we design an efficient privacy-preserving gradient aggregation protocol based on a secure two-party calculation protocol. With the help of a central server, we can accurately recognise the Byzantine attackers and update the global model parameters privately. Furthermore, we theoretically discussed the privacy preservation and efficiency of DisBezant. To verify the effectiveness of our DisBezant, we evaluate it over three real datasets and the results demonstrate that DisBezant can efficiently and effectively achieve the Byzantine-robust federated learning. Although there are 40% nodes are Byzantine attackers in participants, our DisBezant can still recognise them and ensure the accurate model training.

Communication security between IoT devices is a major concern in this area, and the blockchain has raised hopes that this concern will be addressed. In the blockchain concept, the majority or even all network nodes check the validity and accuracy of exchanged data before accepting and recording them, whether this data is related to financial transactions or measurements of a sensor or an authentication message. In evaluating the validity of an exchanged data, nodes must reach a consensus in order to perform a special action, in which case the opportunity to enter and record transactions and unreliable interactions with the system is significantly reduced. Recently, in order to share and access management of IoT devices information with distributed attitude a new authentication protocol based on blockchain is proposed and it is claimed that this protocol satisfies user privacy preserving and security. However, in this paper, we show that this protocol has security vulnerabilities against secret disclosure, replay, traceability, and reuse attacks with the success probability of 1 and constant complexity of also 1. We also proposed an improved blockchain-based authentication protocol (IBCbAP) that has security properties such as secure access management and anonymity. We implemented IBCbAP using JavaScript programming language and Ethereum local blockchain. We also proved IBCbAP’s security both informally and formally through the Scyther tool. Our comparisons showed that IBCbAP could provide suitable security along with reasonable cost.