The booming technologies of the Internet of Things (IoT) enable various applications and develop well. E-healthcare system is one of the successful examples, where people manage their healthcare information, diagnosis data, physical examination results, and so on. These types of sensitive information cause crucial security risks, posing threats to the security of the lives and property of the people. Given this concern, safeguard measures, such as authentication and encryption, are necessary. However, due to the openness of the wireless networks, authentication protocols for IoT-enabled e-healthcare systems are usually vulnerable to serious attacks. Furthermore, the quantum era is around the corner, urging authentication protocols to possess post-quantum security properties. In this paper, we propose a multi-factor password authentication scheme, named PAMA, with post-quantum security for the e-healthcare system. The lattice cryptography is adopted for post-quantum security. We formally prove the proposed PAMA scheme to be secure, and also informally verify the security against common attacks. Furthermore, we compare the performance of the communication efficiency of the PAMA scheme with other related works from both theoretical and experimental aspects. The efficiency comparison results demonstrate that our PAMA scheme approximately reduces the computation cost by 37.59% and the energy consumption by 37.5%, compared to the related post-quantum schemes. In summary, the PAMA scheme has a great advantage in secure authentication and agreement on a session key in the e-healthcare system.

Machine learning is a rapidly evolving field with applications in all aspects of human life. Utilizing the decentralized computing architecture can alleviate the high training and computational burden of central servers and improve service accuracy. However, the inherent properties of decentralized networks pose great challenges to communication security. It is urgent to design novel and appropriate security schemes, as malicious adversaries are curious about user private information, sensing data, and service demands. Furthermore, end devices always cooperate to accomplish the service targets, which means that group security schemes are needed to protect transmissions among them. In this paper, a certificateless-based group authentication and key agreement (CL-GAKA) scheme is proposed, named 2PCLGA, for distributed learning-based mobile edge computing (DL-MEC) networks. The proposed scheme establishes a session key among the end device group with the group leader MEC server based on the elliptic curve cryptography. Besides, the 2PCLGA scheme adopts dynamic pseudonym identity technology to realize the anonymity. The provable security analysis under the random oracle model, the formal analysis tool, and the informal analysis are adopted. The performance of 2PCLGA is also evaluated with the benchmarks, and the results show that the 2PCLGA scheme is greatly applicable to the resource-constrained circumstance.

Few-shot class incremental learning (FSCIL) aims to continuously learn new classes from limited training samples while retaining previously acquired knowledge. Existing approaches are not fully capable of balancing stability and plasticity in dynamic scenarios. To overcome this limitation, we introduce a novel FSCIL framework that leverages graph neural networks (GNNs) to model interdependencies between different categories and enhance cross-modal alignment. Our framework incorporates three key components: 1) a Graph Isomorphism Network (GIN) to propagate contextual relationships among prompts; 2) a Hamiltonian Graph Network with Energy Conservation (HGN-EC) to stabilize training dynamics via energy conservation constraints; and 3) an Adversarially Constrained Graph Autoencoder (ACGA) to enforce latent space consistency. By integrating these components with a parameter-efficient CLIP backbone, our method dynamically adapts graph structures to model semantic correlations between textual and visual modalities. Additionally, contrastive learning with energy-based regularization is employed to mitigate catastrophic forgetting and improve generalization. Comprehensive experiments on benchmark datasets validate the framework's incremental accuracy and stability compared to state-of-the-art baselines. This work advances FSCIL by unifying graph-based relational reasoning with physics-inspired optimization, offering a scalable and interpretable framework. Code is available at: https://github.com/aries-yqian/ACHG-CLIP.

The flourishing development of the Internet of Medical Things (IoMT) is bringing personalized and timely healthcare to an increasing number of people. Fog-cloud-based IoMT pushes computing and data processing to the edge network, enabling low latency and real-time response. However, the heterogeneity of IoMT and medical data privacy make designing secure and efficient authentication schemes a highly challenging task. In this article, we first design a blockchain-assisted fog-cloud-based IoMT authentication architecture, leveraging blockchain technology to enhance the security and efficiency of medical data sharing. Additionally, we propose a novel anonymous authentication scheme that utilizes lightweight cryptographic primitives, physically unclonable functions, and fuzzy extractors suitable for resource-constrained IoMT devices. We use formal and informal security analysis to prove the security of the proposed scheme. Furthermore, we evaluate the performance of the proposed scheme from the perspectives of computation, communication, energy, average delay time, and smart contract resource consumption. Compared to five related schemes, our scheme achieves higher security while maintaining low resource consumption.

Wireless body area networks (WBANs) are technologies that create wireless networks around the human body by embedding sensors into wearable devices to collect health data. WBANs are mainly used for real-time health monitoring, motion tracking, and emergency medical services, with advantages, such as low energy consumption, short-range communication, and high data security. However, user privacy protection and communication security during transmission are important considerations in the design of WBANs communication. To address the aforementioned issues, this article presents a secure authentication key agreement protocol in WBANs. Through Scyther software analysis, it demonstrates the protocol satisfies security attributes, such as user anonymity, known session key security, forward security, resistance to ephemeral key leakage attacks, and resistance to impersonation attacks. The protocol has also been verified to be secure within the eCK model. Moreover, this article compares the security attributes, computation cost, communication consumption, energy consumption, and average delay time of the proposed protocol with other authentication key agreement protocols. It is found that the proposed protocol has excellent performance while ensuring communication security, and strikes a balance between security effectiveness and resource overhead.

Distributed learning (DL) emerges as machine learning and the Internet of Things develop quickly and widely. As edge servers pre-process and pre-learn the statistics, global servers can reduce costs, improve efficiency and output more precise results. However, to acquire high-quality and adequate data, servers should collect information from a number of end devices, which naturally leads to confidentiality and privacy problems during information transmission. If the private information or the data are compromised by malicious attackers, the users’ security and the network operation will all be in danger. To resolve this thorny challenge, numerous schemes have been put forward, adopting different cryptography technologies and aiming at aspects of security. However, many state-of-the-art schemes can hardly satisfy the security demands and are pointed out to be defective. Lately, Jiang et al. made an effort and proposed a certificateless signature scheme, as well as an authentication scheme for the purpose of solving the privacy issues. Unfortunately, in this paper, we point out that their schemes can hardly resist forgery attacks and ephemeral key leakage attacks. Further, we will propose an improved scheme noted as 2PCLA and change the method of generating the session key. Theoretical analysis and formal security analysis utilizing Tamarin analysis tool are provided to prove the security of 2PCLA scheme. Performance evaluation has been done from both theoretical and experimental perspectives. The assessment results illustrate that 2PCLA can balance security properties with execution efficiency relatively well.

The complexity of Web services composition process is intuitively relevant to the effects such as readability, testability, reliability, and maintainability. Analyzing the complexity at all stages of process design and development helps avoid the drawbacks associated with high-complexity processes. In this paper, we present a control-flow complexity measure of the structured process of Web service composition. The measure is defined based on a data structure named Structure Tree. By the complexity computing algorithm based on Structure Tree, we can get complexity of a process and its substructures simultaneously. The characteristics of different structure types and the nesting level are taken into consideration in the complexity measure. Since Weyuker's properties are a widely known formal analytical approach of complexity metric, we evaluate our measure in terms of these properties in order to guarantee its effectiveness.