In the Internet of Medical Things (IoMT), securing patient data access is critical, but must be achieved without overwhelming the limited computational resources of edge devices. While cryptographic methods and access policies are widely applied to secure medical data, existing solutions often assume high computational capacity, centralized infrastructure, or predefined key structures, which are not ideal for the heterogeneous and resource-constrained environments found in IoMT. In addition to security, patient-centricity is becoming an essential design principle, where patients must have control over who accesses their data and under what conditions. Similarly, edge computing has emerged as a means to reduce latency, but edge devices are often semi-trusted, exposed to physical threats, and limited in processing power, making them unsuitable for heavyweight integrity verification or outsourced computation. Therefore, this paper presents a lightweight, patient-centric architecture that unifies attribute-based access control, dynamic edge data integrity verification, and consent-driven sharing in a secure and scalable architecture. The system minimizes communication and computational overhead by eliminating predefined keys, restricting edge computation, and guaranteeing verifiable data delivery. The experimental results demonstrate efficient handling of tampered and untampered cases, achieving fast, verifiable, and secure access with minimal resource consumption. This paper offers a practical and integrative solution to ensure security without sacrificing lightweight performance in real-world IoMT deployments.

Generative artificial intelligence (AI), an advancing frontier, uses machine learning to autonomously create media content, though it faces challenges with bias and fair representation. This research investigates demographic bias within the LAION dataset, specifically focusing on the representation of Indigenous Australians. Large image generative models are typically trained on extensive datasets scraped from the Internet, which often reflect the demographics of the most active online communities rather than accurately representing local populations. While existing studies have examined hate speech, gender balance, and broad ethnic diversity within LAION, limited research addresses representation relative to specific national demographics or minority subgroups. Auditing the dataset with state-of-the-art facial recognition and sentiment analysis models, we assessed the age, gender, and ethnicity of images in LAION and compared these distributions against census data for Australia and the United States. We also conducted a focused analysis of Indigenous Australian representation by identifying relevant images using keyword searches and applying the aforementioned models. Our findings reveal that the dataset demographic composition poorly aligns with the actual population of Australia, with regional subgroups under-represented, potentially leading to inaccurate portrayals of Indigenous Australians. These results underscore the need for either strengthened safeguards on globally developed generative models or the development of locally trained models to ensure responsible and inclusive cultural representation in AI-generated imagery.

The Internet of Medical Things (IoMT) systems enable the continuous monitoring and collection of healthcare data from various medical devices and sensors, facilitating real-time analysis and timely interventions. One such example of the IoMT system is the early and accurate detection of arrhythmia using electrocardiogram (ECG) signals, which plays a crucial role in improving patient health. However, healthcare data contains sensitive information that raises privacy concerns for users. In recent years, Federated Learning (FL) offers a promising solution by enabling collaborative model training on distributed ECG data at the device level while preserving data privacy. However, FL is computationally expensive and suffers from data heterogeneity, which slower convergence, reduces performance, and hinders generalization across diverse client datasets. In this work, we propose a lightweight FL-based model designed explicitly for arrhythmia detection with data heterogeneity. We evaluate our model's performance on two publicly available ECG datasets (PTBD and MIT-BIH arrhythmia). The proposed model achieves high accuracy (between 0.95 and 0.98) while maintaining robustness against heterogeneous data distributions. Furthermore, the experimental results demonstrate significant efficiency gains compared to a baseline model (ResNet). Our proposed lightweight FL-based model requires substantially less mega floating point operations (MFLOPS) (0.07 vs. 2.14 for ResNet) and communication cost (1000 Mb vs. 7500 Mb for ResNet) to achieve convergence. These results indicate the potential of our proposed approach for practical and privacy-preserving arrhythmia detection in resource-constrained IoMT settings.

We propose a blockchain-enabled lightweight federated learning (BlockFL-Med) framework tailored for smart medical spaces, e.g., the Internet of Medical Things (IoMT), addressing key challenges, e.g., privacy preservation, trust management, and scalability. The framework ensures the privacy of sensitive patient data by employing federated learning, where only model updates are shared instead of raw data. To enhance trust, the framework integrates blockchain technology, creating a decentralized and tamper-proof network that verifies client contributions and mitigates risks from malicious participants. Experimental results demonstrate the scalability and efficiency issues by optimizing communication costs, e.g., transmitting lightweight kilobyte-sized model updates instead of larger megabyte-sized models, making it well-suited for heterogeneous and resource-constrained IoMT environments.

The Internet of Medical Things (IoMT) represents a significant technological advancement with exceptional capabilities across various domains, particularly in healthcare. IoMT integrates medical devices, software applications, and healthcare systems, enabling seamless communication and data exchange over the Internet. As deep learning (DL) continues to evolve, applications within IoMT are increasingly dominant. However, these DL applications face new reliability challenges, particularly due to the security threat posed by adversarial attacks. These attacks introduce subtle and often imperceptible perturbations that can lead to significantly erroneous predictions by classifiers. To address these reliability concerns, we propose a novel security mechanism using an attack detector specifically designed to counter adversarial attacks within IoMT environments. This approach leverages a transformer model to enhance resistance against such attacks. We validate our method through experiments using datasets for skin cancer, retina damage, and chest X-rays, testing against both white-box attacks (e.g., Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD)) and black-box attacks (e.g., Additive Gaussian Noise (AGN) and Additive Uniform Noise (AUN)). Our proposed attack detector exhibited F1 and accuracy 0.91 and 0.94. Following the successful application of our attack detector, the disease classification model achieved an average F1 and accuracy of 0.97 and 0.98 compared to the attack model performance (F1 and accuracy of 0.64 and 0.60, respectively) across the three datasets.

Adversarial attacks pose a significant threat to the integrity and reliability of electrocardiogram (ECG) signals, compromising their use in critical applications, e.g., arrhythmia detection and classification. In this paper, we propose an attack-data-independent defence mechanism to effectively mitigate adversarial attacks on ECG signals. Unlike existing defence mechanisms that rely on learning from adversarial samples, our proposed approach operates as a ‘gatekeeper,’ selectively discarding noisy and attack signals while allowing only clean and non-attack ECG signals to be stored in the data layer. This ensures the availability of reliable and high-quality ECG data for subsequent analysis. The proposed defence mechanism not only detects and filters out the attack and noisy ECG signals but also provides robust protection against adversarial attacks, enhancing the integrity and trustworthiness of ECG data for critical applications. To evaluate the effectiveness of our proposal, we conduct experiments using physiologic and synthetic ECG datasets against two well-known attacks: a white-box attack (Fast Gradient Signed Method (FGSM) and Projected Gradient Descent (PGD)) and a black-box attack (HopSkipJump and Boundary). Our experimental results demonstrate the superiority and effectiveness of our approach in defending against adversarial attacks on ECG signals, making it a promising solution for ensuring the security and reliability of ECG-based diagnosis in smart healthcare applications.

Fog computing plays a prominent role in offloading computational tasks in heterogeneous environments since it provides less service delay than traditional cloud computing. The Internet of Things (IoT) devices cannot handle complex tasks due to less battery power, storage and computational capability. Full offloading has issues in providing efficient computation delay due to more response time and transmission cost. A suitable solution to overcome this problem is to partition the tasks into splittable subtasks. Considering multi-criteria decision parameters like processing efficiency and deadline helps to achieve efficient resource allocation and task assignment. The matching theory is applied to map task nodes to heterogeneous fog nodes and VMs for stability. Compared to baseline algorithms, proposed algorithms like Resource Allocation based on Processing Efficiency (RABP) and Task Assignment Based on Completion Time (TAC) are efficient enough to provide reasonable service delay and discard the non-beneficial tasks, i.e., tasks that do not execute within the deadline.

Given the escalating diversity, sophistication, and frequency of cyber attacks, it is imperative for critical infrastructure entities, e.g. smart grids, to recognize the inherent risks of operating in isolation. Sharing cyber threat intelligence (CTI) helps them stand together and build a collective cyber defense by knowledge, skills, and experience encompassing information related to identifying and evaluating cyber and physical threats. The present studies lack on robust CTI sharing strategies in smart grid systems. To address the critical need for secure and effective CTI sharing in smart grid systems, this article proposes a novel approach. Our solution leverages encrypted federated learning (FL) with integrated malicious client detection mechanisms. This approach facilitates collaborative learning of a threat detection model while preserving the privacy of raw CTI data. Employing real-world, heterogeneous smart grid datasets, we rigorously evaluated our approach under two distinct attack scenarios. The results demonstrate resilience against both man-in-the-middle attacks and malicious clients, exceeding the performance typically observed in traditional FL models.