As biometrics-based identification and identity authentication become increasingly widespread in their deployment, it becomes correspondingly important to consider more carefully issues relating to reliability, usability and inclusion. One factor which is particularly important in this context is that of the relationship between the nature of the measurements extracted from a particular biometric modality and the age of the sample donor, and the effect which age has on physiological and behavioural characteristics invoked in a biometric transaction. In Age Factors in Biometric Processing an international panel of experts explore the implications of ageing on biometric technologies, and how such factors can be managed in practical situations. Topics include understanding the impact of ageing on biometric measurements; age factors as barriersopportunities in relation to performance; modality-related approaches to management of age factors; implications for practical application; and future trends and research challenges. Age Factors in Biometric Processing provides an outstanding overview of this topic for the rapidly expanding community of stakeholders in biometricsbased identification solutions in academia, industry and government.

Critical systems and infrastructure rely heavily on ICT systems and networks where security issues are a major concern. Authentication methods verify that messages come from trusted sources and guarantee the smooth flow of information and data. In this edited reference, the authors present state-of-art research and development in authentication technologies including challenges and applications for Cloud Technologies, IoT and Big Data. Topics covered include authentication; cryptographic algorithms; digital watermarking; biometric authentication; block ciphers with applications in IoT; identification schemes for Cloud and IoT; authentication issues for Cloud applications; cryptography engines for Cloud based on FPGA; and data protection laws.

Cloud Computing has already been embraced by many organizations and individuals due to its benefits of economy, reliability, scalability and guaranteed quality of service among others. But since the data is not stored, analysed or computed on site, this can open security, privacy, trust and compliance issues. This one-stop reference covers a wide range of issues on data security in Cloud Computing ranging from accountability, to data provenance, identity and risk management. Data Security in Cloud Computing covers major aspects of securing data in Cloud Computing. Topics covered include NOMAD: a framework for ensuring data confidentiality in mission-critical cloud based applications; 3DCrypt: privacy-preserving pre-classification volume ray-casting of 3D images in the cloud; multiprocessor system-on-chip for processing data in Cloud Computing; distributing encoded data for private processing in the cloud; data protection and mobility management for cloud; understanding software defined perimeter; security, trust and privacy for Coud Computing in transportation cyber-physical systems; review of data leakage attack techniques in cloud systems; Cloud Computing and personal data processing: sorting out legal requirements; the Waikato data privacy matrix; provenance reconstruction in clouds; and security visualization for Cloud Computing.

Hand-based biometrics identifies users by unique features in their hands, such as fingerprints, palmprints, hand geometry, and finger and palm vein patterns. This book explores the range of technologies and methods under development and in use for handbased biometrics, with evaluations of the advantages and performance of each. The inclusion of significant material on the relevant aspects of the physiology of the hand is a particularly useful and innovative feature. Topics covered in this book include inner and outer hand physiology and diseases; nail structure and common disorders; fingerprint recognition; synthetic fingerprints; finger vein recognition; palm vein biometrics; hand shape recognition and palm print recognition; 3D hand shape recognition; and spoofing and anti-spoofing methods. With contributions from an international panel of experts in this field, Hand-Based Biometrics is essential reading for researchers, students and engineers working in biometrics and security.

Iris recognition technologies for identity management are already deployed globally in several large-scale nationwide biometric projects and are currently entering the mobile market. More recently, periocular recognition has been employed to augment the biometric performance of the iris in unconstrained environments where only the ocular region is present in the image. Iris and Periocular Biometric Recognition provides an overview of scientific fundamentals and principles of iris and periocular biometric recognition over six broad areas: an introduction to iris and periocular recognition; a selective overview of issues and challenges; soft biometric classification; security aspects; privacy protection and forensics; and future trends. With contributions from experts in industry and academia, this book is essential reading for researchers, graduate students and practitioners in biometrics and related fields.

Mobile biometrics - the use of physical and/or behavioral characteristics of humans to allow their recognition by mobile/smart phones - aims to achieve conventional functionality and robustness while also supporting portability and mobility, bringing greater convenience and opportunity for its deployment in a wide range of operational environments from consumer applications to law enforcement. But achieving these aims brings new challenges such as issues with power consumption, algorithm complexity, device memory limitations, frequent changes in operational environment, security, durability, reliability, and connectivity. Mobile Biometrics provides a timely survey of the state of the art research and developments in this rapidly growing area. Topics covered in Mobile Biometrics include mobile biometric sensor design, deep neural network for mobile person recognition with audio-visual signals, active authentication using facial attributes, fusion of shape and texture features for lip biometry in mobile devices, mobile device usage data as behavioral biometrics, continuous mobile authentication using user phone interaction, smartwatch-based gait biometrics, mobile four-fingers biometrics system, palm print recognition on mobile devices, periocular region for smartphone biometrics, and face anti-spoofing on mobile devices.

Wireless physical layer (PHY) security has attracted much attention due to the broadcast nature of the wireless medium and its inherent vulnerability to eavesdropping, jamming, and interference.

Physical Layer Security for Wireless Sensing and Communication covers both communication and sensing security from a broad perspective. The main emphasis is on PHY security, although other security measures are covered for the sake of completeness and as a step towards cross-layer security and cognitive security vision. After discussing the features of wireless channels from both the communication and sensing perspectives, the book details their exploitation for secure transmission utilizing various approaches. Wireless sensing and radio environment concepts are also addressed, along with the related security implications in terms of eavesdropping, disruption, manipulation, and, in general, the exploitation of wireless sensing by unauthorised users. Several solutions for these threats from the domains of wireless communication, military radars, and machine learning, are discussed.

The book provides valuable information to researchers in academia and industry, as well as engineers, developers, and advanced students in the field of cybersecurity.

A consensus mechanism is the core component of a blockchain network, which ensures that every participant agrees on the state of the network in trustless environments. Until now, current blockchain networks have been using the proof-of-work (PoW) consensus mechanism, which has serious limitations such as huge energy consumption, low transaction processing capabilities, and centralization and scalability issues. To overcome these problems, a new consensus mechanism entitled proof-of-stake (PoS) has been developed. PoS has many advantages, including negligible energy consumption and very low consensus delay. Ethereum (the largest decentralized global software platform to create secured digital technology powered by blockchain technology and the blockchain of choice globally for all developers and enterprises) has just switched from PoW to PoS. As a result, this mechanism is expected to become a cutting-edge technology for future blockchain networks.

This book provides a comprehensive discussion of the PoS consensus mechanism for blockchain networks. Starting with an overview of blockchain technology and consensus mechanisms, including basic concepts and network architecture, the book provides a review of the PoS consensus mechanism, including PoS consensus security and PoS performance and scalability issues.

Proof-of-Stake for Blockchain Networks: Fundamentals, challenges and approaches is a valuable resource for researchers, engineers, and postgraduate students who are interested in advanced PoS consensus mechanisms, as well as developers and entrepreneurs who are interested in developing applications using the PoS consensus mechanism.

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.

The split learning method mitigates two fundamental drawbacks of federated learning: affordability, and privacy and security. When running machine learning computation on devices with limited resources, assigning only a portion of the network to train at the client-side minimizes the processing burden, compared to running a complete network as in federated learning. In addition, neither client nor server has full access to the other, which is more secure.

This book reviews cutting edge technologies and advanced research in split federated learning. Coverage includes approaches to realizing and evaluating the effectiveness and advantages of federated learning and split-fed learning, the role of this technology in advancing and securing IoTs, advanced research on emerging AI models for preserving the privacy of the data owned by the clients, and the analysis and development of AI mechanisms in IoT architectures and applications. The use of split federated learning in natural language processing, recommendation systems, healthcare systems, emotion detection, smart agriculture, smart transportation and smart cities is discussed.

Split Federated Learning for Secure IoT Applications: Concepts, frameworks, applications and case studies offers useful insights to the latest developments in the field for researchers, engineers and scientists in academia and industry, who are working in computing, AI, data science and cybersecurity with a focus on federated learning, machine learning and deep learning.

Voice biometrics are being implemented globally in large scale applications such as remote banking, government e-services, transportation and building security access, autonomous vehicles, and healthcare. They have been integrated in numerous apps, often coupled with face biometrics and artificial intelligence methods. Voice biometrics products and solutions must meet three key re-quirements for the success in their deployment: they must be highly trustable regarding privacy protection; easy to use and always be available. This edited book presents the state of the art in voice biometrics research and technologies including implementation and deployment challenges in terms of interoperability, scalability and performance, and security. The team of editors and chapter authors combine a wealth of expertise from academia and the industry. Topics covered include the fundamentals of voice biometrics; design of countermeasures for replay attack; attacker's perspective for voice biometrics; voice biometrics; speaker de-identification; performance evaluation of voice biometrics solutions; standardization of voice biometrics technology; industry perspectives; joining forces of voice and facial biometrics; and future trends and challenges in voice biometrics. Providing comprehensive coverage of the field of voice biometrics, this authoritative volume will be of great interest to researchers, scientists, engineers, practitioners and advanced students involved in the fields of security, biometrics, forensic sciences, human computer interaction, speech processing, acoustics, multimedia, pattern recognition, and privacy-preserving, digital signal processing and speech technologies. It will also be of interest to researchers and professionals working in law and criminology.