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Healthcare systems today are increasingly reliant on data gathered from multiple hospital systems, patient records or IoT devices. As more information is gathered, there is a need to ensure the data is kept and used securely. This edited book looks at secure big data analytics for healthcare and how the wealth of information is disseminated through open wireless channels to provide seamless coverage so that people can access and analyse the results obtained and intelligently manage and respond to a patient's needs.

Fifth generation (5G) wireless networks are now commercialized, and the research focus has shifted towards sixth generation (6G) wireless systems. The integration of sensor nodes and massive machine type communication (MTC) devices (MDs) in ubiquitous 5G networks has facilitated the design of critical enabling technologies to support billions of data-hungry applications. By leveraging sensor nodes in wireless sensor networks (WSNs), sensitive user information can be harvested and transmitted to receivers via WSN-assisted channels, which are often not well secured. Consequently, sensitive user information can be intercepted and used unlawfully. The security and confidentiality measures used for data transmission over existing 5G WSN-assisted channels are limited. 6G systems are envisaged to face fiercer security challenges. In 6G wireless networks, a new set of sensing and precise localization techniques are predicted. Thus, the need to secure user information against adversarial attacks needs to be implemented at the design stage.

As big data becomes increasingly pervasive and cloud computing utilization becomes the norm, the security and privacy of our systems and data becomes more critical with emerging security and privacy threats and challenges. This book presents a comprehensive view on how to advance security and privacy in big data, cloud computing, and their applications. Topics include cryptographic tools, SDN security, big data security in IoT, privacy preserving in big data, security architecture based on cyber kill chain, privacy-aware digital forensics, trustworthy computing, privacy verification based on machine learning, and chaos-based communication systems. This book is an essential reading for networking, computing, and communications professionals, researchers, students and engineers, working with big data and cloud computing.

Hospitals, medical practices and healthcare organizations are implementing new technologies at breakneck speed. Yet privacy and security considerations are often an afterthought, putting healthcare organizations at risk of data security and privacy issues, fines, damage to their reputations, with serious potential consequences for the patients. Electronic Health Record systems (EHRs) consist of clinical notes, patient listings, lab results, imaging results and screening tests. EHRs are growing in complexity over time and requiring increasing amounts of data storage.

New approaches in federated learning and split learning have the potential to significantly improve ubiquitous intelligence in internet of things (IoT) applications. In split federated learning, the machine learning model is divided into smaller network segments, with each segment trained independently on a server using distributed local client data.