The evolution of Patient-Generated Health Data (PGHD) represents a major shift in healthcare, fueled by technological progress. The advent of PGHD, with technologies such as wearable devices and home monitoring systems, extends data collection beyond clinical environments, enabling continuous monitoring and patient engagement in their health management. Despite the growing prevalence of PGHD, there is a lack of clear understanding among stakeholders about its meaning, along with concerns about data security, privacy, and accuracy. This article aims to thoroughly review and clarify PGHD by examining its origins, types, technological foundations, and the challenges it faces, especially in terms of privacy and security regulations. The review emphasizes the role of PGHD in transforming healthcare through patient-centric approaches, their understanding, and personalized care, while also exploring emerging technologies and addressing data privacy and security issues, offering a comprehensive perspective on the current state and future directions of PGHD. The methodology employed for this review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and Rayyan, AI-Powered Tool for Systematic Literature Reviews. This approach ensures a systematic and comprehensive coverage of the available literature on PGHD, focusing on the various aspects outlined in the objective. The review encompassed 36 peer-reviewed articles from various esteemed publishers and databases, reflecting a diverse range of methodologies, including interviews, regular articles, review articles, and empirical studies to address three RQs exploratory, impact assessment, and solution-oriented questions related to PGHD. Additionally, to address the future-oriented fourth RQ for PGHD not covered in the above review, we have incorporated existing domain knowledge articles. This inclusion aims to provide answers encompassing both basic and advanced security measures for PGHD, thereby enhancing the depth and scope of our analysis.

The escalating digital interconnectivity of Critical Infrastructure (CI) necessitates robust cybersecurity solutions, particularly for effective incident detection and response. This systematic literature review explores the promising role of Digital Twins (DTs) in enhancing these critical functions across diverse CI sectors, including smart cities, healthcare, and energy. Covering research from 2019 onwards, we conduct an in-depth analysis of 27 curated articles, examining attacks, detection methods, response techniques, and emerging trends specifically related to incident handling within this domain. Our review reveals DTs as a powerful tool for CI cybersecurity, enabling proactive threat simulation and improved incident management capabilities. However, challenges persist, including scalability, interoperability with legacy systems, and the need for rigorous real-world validation of incident response effectiveness. Furthermore, we identify common shortcomings in existing research, such as algorithmic flaws, inadequate validation, poor metric reporting, and insufficient threat modeling, all of which impact incident-handling capabilities. Despite these limitations, our systematic analysis underscores that DTs can significantly bolster CI security for incident detection and response when these challenges are addressed. To our knowledge, this work provides the first full-fledged survey uniquely and specifically focused on DT-enabled cybersecurity incident detection and response within CI contexts, addressing a critical gap not comprehensively covered by prior reviews and serving as a foundational resource for stakeholders.

This paper provides a comprehensive exploration of the threats and risk associated with the use of digital technology for remote healthcare. Through our "DigiRemote" research project, we aim to find out the challenges and risks involved in scaling remote health care, with specific focus on its technical and security dimensions. The article delves into the use of digital technologies, including Bluetooth-enabled IoMT devices, for monitoring health metrics in remote care settings. Emphasis is placed on understanding the threats in the generation, storage, and transfer of health data. By, utilizing the STRIDE (Spoofing, Tampering, Repudiation, Information disclosure, Denial of service, and Elevation of privilege) model, we identified major threats, which are visually represented via heatmaps and bar graphs for ease of analysis. The DREAD (Damage, Reproducibility, Exploitability, Affected Users, and Discoverability) model assists in quantifying these threats, categorizing them into distinct risk levels, with detailed description of each threat category. In addition, we also suggest countermeasures for the highest-risk threats. This article serves as a pivotal resource for healthcare organizations and security professionals, shedding light on the risks of remote healthcare and providing mitigation strategies for both the implementation and post-implementation stages.

Internet of Things (IoT) devices have changed how billions of people in the world connect and interact with each other. But, as more people use IoT devices, many questions arise about how these devices handle private data and whether they properly ask for permission when using it. Due to information privacy regulations such as the EU’s General Data Protection Regulation (GDPR), which requires companies to seek permission from data subjects (DS) before using their data, it is crucial for IoT companies to obtain this permission correctly. However, this can be really challenging in the IoT world because people often find it difficult to interact with and manage multiple IoT devices under their control. Also, the rules about privacy are not always clear. As such, this paper proposes a new model to improve how consent is managed in the world of IoT. The model seeks to minimize “consent fatigue” (when people get tired of always being asked for permission) and give DS more control over how their data are shared. This includes having default permission settings, being able to compare similar devices, and, in the future, using AI to give personalized advice. The model allows users to easily review and change their IoT device permissions if previous conditions are not met. It also emphasizes the need for easily understandable privacy rules, clear communication with users, and robust tracking of consent for data usage. By using this model, companies that provide IoT services can do a better job of protecting user privacy and managing DS consent. In addition, companies can more easily comply with data protection laws and build stronger relationships with their customers.

Summary: Real‐time, lightweight, adaptive time series anomaly detection is increasingly critical in cybersecurity, industrial control, finance, healthcare, and many other domains due to its capability to promptly process time series and detect anomalies without requiring extensive computation resources. While numerous anomaly detection approaches have emerged recently, they generally employ a single type of recurrent neural network (RNN) and are implemented using a single type of deep learning framework. The impacts of using various RNN types across different deep learning frameworks on the performance of these approaches remain unclear due to a lack of comprehensive evaluations. In this article, we aim to investigate the impact of different RNN variants and deep learning frameworks on real‐time, lightweight, and adaptive time series anomaly detection. We reviewed several state‐of‐the‐art anomaly detection approaches and implemented a representative approach using several RNN variants supported by three popular deep learning frameworks. A thorough evaluation was conducted to analyze the detection accuracy, time efficiency, and resource consumption of each implementation using four real‐world, open‐source time series datasets. The results show that RNN variants and deep learning frameworks have a significant impact. Therefore, it is crucial to carefully select appropriate RNN variants and deep learning frameworks for the implementation.