In the past few years, the deployment of sensor networks and the Internet of Things (IoT) has grown significantly. This continuous and exponential growth is facilitated by investments and research activities originating from industry, academia, and governments, while the penetration of these technologies is also driven by the high technology acceptance rates of both consumers and technologists across disciplines. Such networks collect, store, and exchange a large volume of heterogeneous societal data specifically for the health and medical domain. Nevertheless, their rapid and widespread deployment and their participation in the provisioning of potentially critical healthcare and industrial services raise numerous issues related to the security and data analysis of the performed operations and provided services.

IoT technologies have become increasingly in demand in healthcare for development, testing, and trials, with uses both in hospitals and homes. End-to-end clinical data connectivity involves developing many technologies that should enable reliable and location-agnostic communication between a patient and a healthcare provider. However, the main challenge in healthcare is how to manage concerning critical applications, where several connected devices generate a large amount of medical data. This large volume of data, often called big data, cannot readily be processed by traditional data processing algorithms and applications over a secured intelligent communication network. By investigating and collecting large amounts of healthcare data (i.e., big data), medical IoT can enhance the decision-making process and early disease diagnosis. Accordingly, research on the data analysis and security of medical IoT is attracting increasing attention from both industry and academia.

In line with these efforts, the central theme of this Special Issue is to report novel methodologies, theories, technologies, techniques, and solutions for healthcare data security and analytics techniques for a sustainable smart society. This Special Issue aims to address these topics across multiple abstraction levels, ranging from architectural models, the provisioning of services, protocols, and interfaces to specific implementation approaches. Furthermore, additional focus will be given to areas related to the role of data mining and machine learning in modeling and deploying intelligent and secure healthcare data-driven smart society (SHDS). We aim to present the most important and relevant advances to overcome the challenges related to security, data analytics, communication networks, and energy-aware solutions in the Internet of Things. Original research and review articles are welcome.

Potential topics include but are not limited to the following:

• Novel security architectures, protocols, or applications for SHDS
• Privacy preservation and vulnerability analysis in the SHDS
• Threat modelling and risk assessment in smart society
• Energy-aware secure communications in SHDS
• Intrusion detection, disaster recovery, and forensics for SHDS
• Privacy-preserving, machine-learning-based data analytics in SHDS
• Security testbeds and experimental results for SHDS
• Lightweight security protocols and architectures for SHDS
• Privacy-enhancing and anonymization techniques in sensor networks and the SHDS
• Trust and identity management in SHDS
• Access control for shared data in IoT devices
• Software-Defined Networking for SHDS
• Machine learning, data mining, and blockchain-based solutions for SHDS
• Distributed data mining and machine learning systems for secured SHDS
• Security management and security of mobile solutions for SHDS