Intelligent Techniques for Security of Medical Data

The rapid digital transformation of healthcare has created unprecedented opportunities for improving patient outcomes, operational efficiency, and accessibility of medical services. At the same time, the increasing reliance on connected devices, cloud infrastructures, and data-driven decision-making has introduced significant challenges related to security, privacy, trust, and regulatory compliance. Emerging technologies such as artificial intelligence, machine learning, blockchain, federated learning, post-quantum cryptography, and neuromorphic computing are playing a pivotal role in addressing these challenges while enabling the next generation of intelligent healthcare systems. Their potential extends from secure data sharing and privacy-preserving analytics to predictive diagnostics, personalized medicine, remote patient monitoring, and autonomous healthcare applications. The book is intended for undergraduate and postgraduate students, researchers, academicians, healthcare professionals, cybersecurity practitioners, AI engineers, data scientists, and industry experts working in the fields of artificial intelligence, cybersecurity, healthcare informatics, medical data analytics, blockchain technologies, Internet of Things (IoT), and intelligent healthcare systems. It also serves as a valuable reference for professionals seeking to understand the practical implementation of advanced computational and security technologies in modern healthcare environments. Key Features: Explores novel cutting-edge techniques for the protection of health data. Provides a better understanding and analysis of the telemedicine applications with these techniques. Discusses the integration of intelligent systems with cutting edge techniques for e- health care management. Reviews the development of the different state of the art techniques for social good. Examines how the developed techniques will result in better remote health care services with improved and efficient diagnosis.

Prof. George A. Papakostas has a Diploma (1999), MSc (2002), and PhD (2007) in electrical and computer engineering from the Democritus University of Thrace (DUTh), Greece. He is now a tenured Full Professor with the Department of Informatics, DUTh, Kavala, Greece. He has 15 years of experience in large-scale systems design, as a senior software engineer and a technical manager. He is also the Head of the Machine Learning and Vision (MLV) Research Group. He has authored more than 300 publications in indexed journals, international conferences, and book chapters, one book (in Greek), two edited volumes, and ten journal special issues. His publications have more than 7300 citations with an H-index of 46 (Google Scholar). Prof. Papakostas is a top 2% of researchers worldwide for the years 2022, 2023 and 2024 in the field of “Artificial Intelligence & Image Processing” according to the Stanford/Elsevier List. His research interests include machine learning, computer/machine vision, pattern recognition, and computational intelligence. He is a member of the IAENG, MIR Labs, EUCogIII, and the Technical Chamber of Greece (TEE). He has served as a reviewer for numerous journals and conferences.

Dr. Weiwei Jiang has a BSc (2013) and PhD (2018) from the Department of Electronic Engineering, Tsinghua University, Beijing, China. He is currently an assistant professor with the School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, and Key Laboratory of Universal Wireless Communications, Ministry of Education, China. His current research interests include artificial intelligence, machine learning, big data, wireless communication and edge computing. He has published more than 60 academic papers in IEEE Trans and other journals, with more than 3500 citations in Google Scholar. He is a Top 2% scientist in Stanford's List in 2023 and 2024. In addition to his research endeavours, Dr. Jiang actively engages with scholarly journals as an editor. He is also the Guest Editor for many journals.

Prof. Vinay Kumar Srivastava has a BE in Electronics and Telecommunication Engineering (1989) from Government Engineering College Rewa, MP, India, a MTech in Communication Engineering from IIT BHU (1991), Varanasi, India, and a PhD in Electrical Engineering (2001) from IIT Kanpur, India. After spending a brief period in Indian Telephone Industries Limited, Naini, Allahabad, as an Assistant Executive Engineer, he joined Motilal Nehru National Institute of Technology, Allahabad, India, as a Lecturer in 1992, where he became an Assistant Professor in 2001, Associate Professor in 2006 and Professor in 2010. He has about thirty years of teaching and research experience. He has supervised several B Tech projects, M Tech Theses, and PhD Theses. His research areas include signal and image processing, digital image watermarking, compression, multimedia security, and data hiding.

Dr. Priyank Khare has a BTech in Electronics & Communication Engineering (2009) from Uttar Pradesh Technical University, MTech in Digital Systems (2014), and a PhD in Electronics & Communication Engineering from the Motilal Nehru National Institute of Technology, Allahabad, India. He is currently an assistant professor in the electronics and communication engineering department at the Indian Institute of Information Technology in Ranchi, India. His research interests include digital image processing, digital image watermarking, multimedia security, and data hiding techniques.

Dr. Divyanshu Awasthi has a BTech in ECE from the University of Allahabad, India (2018), MTech in Signal Processing from Motilal Nehru National Institute of Technology Allahabad, India (2021), and a PhD from the Department of Electronics and Communication Engineering at Motilal Nehru National Institute of Technology Allahabad, India. Currently he an assistant professor in GLA University Mathura, India. He is working on projects related to Digital Image Watermarking. His areas of interest are digital image processing, signal processing, data hiding, cryptography, and deep learning.