Human activity recognition systems (HARSs) are vital in a wide range of real-life applications and are a vibrant academic research area. Although they are adopted in many fields, such as the environment, agriculture, and healthcare and they are considered assistive technology, they seem to neglect the aspects of security and privacy. This problem occurs due to the pervasive nature of sensor-based HARSs. Sensors are devices with low power and computational capabilities, joining a machine learning application that lies in a dynamic and heterogeneous communication environment, and there is no generalized unified approach to evaluate their security/privacy, but rather only individual solutions. In this work, we studied HARSs in particular and tried to extend existing techniques for these systems considering the security/privacy of all participating components. Initially, in this work, we present the architecture of a real-life medical IoT application and the data flow across the participating entities. Then, we briefly review security and privacy issues and present possible vulnerabilities of each system layer. We introduce an architecture over the communication layer that offers mutual authentication, solving many security and privacy issues, particularly the man-in-the-middle attack (MitM). Relying on the proposed solutions, we manage to prevent unauthorized access to critical information by providing a trustworthy application.

Nowadays, assistive technologies, especially in the health sector, are entering more and more into our lives. While their primary goal is to enhance human well-being, they also pose potential risks that could significantly impact the security and privacy of individuals. Our initiative targets the creation of a secure ecosystem specifically tailored to a healthcare application. Leveraging cutting-edge technologies such as p-ABC credentials and sophisticated wallet applications, in alignment with emerging European regulations and eIDAS standards we fortify data integrity and user privacy. Our development will be built over blockchain technology offering a decentralized approach and establishing trust within the participating entities. Furthermore, since these systems utilize healthcare data, our solution integrates Federated Learning to establish data privacy during data transmission and training.

Metaverse presents significant opportunities for educational advancement by facilitating immersive, personalized, and interactive learning experiences through technologies such as virtual reality (VR), augmented reality (AR), extended reality (XR), and artificial intelligence (AI). However, this potential is compromised if digital environments fail to uphold individuals’ privacy, autonomy, and equity. Despite their widespread adoption, the privacy implications of these environments remain inadequately understood, both in terms of technical vulnerabilities and legislative challenges, particularly regarding user consent management. Contemporary Metaverse systems collect highly sensitive information, including biometric signals, spatial behavior, motion patterns, and interaction data, often surpassing the granularity captured by traditional social networks. The lack of privacy-by-design solutions, coupled with the complexity of underlying technologies such as VR/AR infrastructures, 3D tracking systems, and AI-driven personalization engines, makes these platforms vulnerable to security breaches, data misuse, and opaque processing practices. This study presents a structured literature review and comparative analysis of privacy risks, consent mechanisms, and digital boundaries in metaverse platforms, with particular attention to educational contexts. We argue that privacy-aware design is essential not only for ethical compliance but also for supporting the long-term sustainability goals of digital education. Our findings aim to inform and support the development of secure, inclusive, and ethically grounded immersive learning environments by providing insights into systemic privacy and policy shortcomings.

The growing influence of technology in the healthcare industry has led to the creation of innovative applications that improve convenience, accessibility, and diagnostic accuracy. However, health applications face significant challenges concerning user privacy and data security, as they handle extremely sensitive personal and medical information. Privacy-Enhancing Technologies (PETs), such as Privacy-Attribute-based Credentials, Differential Privacy, and Federated Learning, have emerged as crucial tools to tackle these challenges. Despite their potential, PETs are not widely utilized due to technical and implementation obstacles. This research introduces a comprehensive framework for protecting health applications from privacy and security threats, with a specific emphasis on gamified mental health apps designed to manage Attention Deficit Hyperactivity Disorder (ADHD) in children. Acknowledging the heightened sensitivity of mental health data, especially in applications for children, our framework prioritizes user-centered design and strong privacy measures. We suggest an identity management system based on blockchain technology to ensure secure and transparent credential management and incorporate Federated Learning to enable privacy-preserving AI-driven predictions. These advancements ensure compliance with data protection regulations, like GDPR, while meeting the needs of various stakeholders, including children, parents, educators, and healthcare professionals.