Paperback. The digital transformation of healthcare has generated vast amounts of sensitive data, from electronic health records and medical images to continuous signals from wearable devices. While this data holds immense promise for advancing precision medicine and clinical research, its sensitive nature raises pressing concerns about privacy, security, and regulatory compliance. Traditional centralized approaches to data sharing often increase risks of breaches and restrict collaboration across institutions. Emerging solutions such as federated learning, which enables collaborative model training without exposing raw data, and generative AI, which creates realistic synthetic datasets to mitigate privacy risks, are redefining how health information can be managed responsibly. Managing Sensitive Health Data Through Federated Learning and Generative AI: Privacy Preserving Techniques provides a comprehensive understanding of how federated learning and generative AI can be applied to manage sensitive health data while preserving privacy, security, and regulatory compliance. This book equips practitioners with practical frameworks, case studies, and emerging techniques that balance the need for data-driven innovation with the ethical responsibility of protecting patient confidentiality. Covering topics such as cross-institutional healthcare collaboration, futuristic image processing techniques, and quantum-safe encryption, this book is a critical academic resource for graduate and doctoral students, healthcare professionals, researchers, data scientists, policymakers, and more. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

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Detalles bibliogr�ficos

T�tulo: Managing Sensitive Health Data Through Federated Learning and Generative AI: Privacy Preserving Techniques (Paperback)
Editor: IGI Global, Hershey
Fecha de publicaci�n: 2026
Idioma: Ingl�s
ISBN 13: 9798337374277
Encuadernaci�n: Paperback
Estado: new

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Sinopsis

The digital transformation of healthcare has generated vast amounts of sensitive data, from electronic health records and medical images to continuous signals from wearable devices. While this data holds immense promise for advancing precision medicine and clinical research, its sensitive nature raises pressing concerns about privacy, security, and regulatory compliance. Traditional centralized approaches to data sharing often increase risks of breaches and restrict collaboration across institutions. Emerging solutions such as federated learning, which enables collaborative model training without exposing raw data, and generative AI, which creates realistic synthetic datasets to mitigate privacy risks, are redefining how health information can be managed responsibly. Managing Sensitive Health Data Through Federated Learning and Generative AI: Privacy Preserving Techniques provides a comprehensive understanding of how federated learning and generative AI can be applied to manage sensitive health data while preserving privacy, security, and regulatory compliance. This book equips practitioners with practical frameworks, case studies, and emerging techniques that balance the need for data-driven innovation with the ethical responsibility of protecting patient confidentiality. Covering topics such as cross-institutional healthcare collaboration, futuristic image processing techniques, and quantum-safe encryption, this book is a critical academic resource for graduate and doctoral students, healthcare professionals, researchers, data scientists, policymakers, and more.

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Manisha Guduri is currently a tenure-track Assistant Professor at the Department of Electrical and Computer Engineering, Lawrence Technological University, MI, USA. Previously, she worked as a full-time instructor at the University of Louisiana at Lafayette, USA. She is the author/ coauthor of more than 74 research papers in reputed journals, book chapters, and international conferences. Her research interests include Artificial Intelligence, Biomedical Applications, VLSI/CAD design. She is currently working on VLSI and AI in the biomedical field. She published 5 patents out of which 2 are under FER. She received three patent grants. She is the reviewer of IEEE TVLSI, Microelectronics Journal, IET digital circuits, IEEE Journal of Biomedical and Health Informatics, etc. She has one on-going funded project from the Department of Science and Technology. She is a senior member of IEEE, USA. She is also currently member of various IEEE Societies such as IEEE Young Professionals, IEEE Women in Engineering, Circuits and Systems, Computer Society, Sensor Council, etc. She was IEEE Lafayette Section Chair for 2025. She was IEEE USA Awards and Recognition Committee Member in 2025. She is the Associate Editor- Digital Media of IEEE JETCAS for 2026. She is Associate Editor for IEEE Transactions on Industrial Informatics and the lead guest editor in Special Issue - IEEE Transactions on Consumer Electronics. She is appointed as IEEE WiE CASS representative for 2023 & 2024. She is IEEE WiE DL program Coordinator and IEEE Computer Society Lafayette section Vice Chair for 2024. She has delivered more than 35 invited talk/tutorial speech/expert talk in various platforms like International Conference /technical programs. She has organized 10 international conferences under different roles.

George Pappas is an Associate Professor in the Department of Electrical and Computer Engineering (ECE) and is currently working with several graduate and undergraduate students on research in a wide range of emerging areas, from automotive to medical applications. Dr. Pappas is also the Director of the Master of Science (M.S.) in Artificial Intelligence (AI) program. He has over 15 years of teaching, research, and industry experience in embedded systems and high-performance computing. He also investigates encryption and optimization algorithms, as well as the security of electronic medical data transfer using wireless cellular communication systems for evaluation, diagnosis, and treatment of patients in remote locations. His research interests include artificial intelligence (AI) in radiology, specifically computerized tomography (CT) image reconstruction; advanced data analytics for pathology images; virtual reality (VR) in medical applications;

Sandeep Thota is an accomplished Senior Member of Technical Staff at Oracle with over 7 years of experience in software engineering and healthcare IT innovation. He currently leads the development of the Semantic Index, a groundbreaking data stack enhancing Electronic Health Records with AI and ML to improve patient care and reduce operational costs. His contributions have directly advanced the state of healthcare technology through patented solutions, IEEE peer-reviewed publications, and invited conference papers. As an IEEE Senior Member, Sandeep actively contributes to the professional community by reviewing technical papers, mentoring aspiring engineers, and judging hackathons. His work has not only delivered measurable impact within industry but has also influenced the broader research and engineering ecosystem.

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