Managing Sensitive Health Data Through Federated Learning and Generative AI: Privacy Preserving Techniques

Editors

Manisha Guduri (Editor), George Pappas (Editor), and Sandeep Thota (Editor)

ISBN: 9798337374260

Published: June, 2026

Format: Hardcover

Language: English

Publisher: IGI Global Scientific Publishing

Description

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.

Key Features

• Practical frameworks for deploying federated learning across multiple hospitals without moving raw patient data off-site — directly applicable to multi-institutional research consortia and hospital networks.
• Dedicated coverage of quantum-safe encryption for healthcare data, preparing institutions for compliance requirements as quantum computing threats to current encryption standards become more concrete.
• Generative AI methods for producing synthetic patient datasets, letting research and IT teams train and test models without exposing real patient records.

Coverage

Blockchain, Cross-Institutional Healthcare Collaboration, Federated Learning, Generative Artificial Intelligence (GenAI), Healthcare Communication, Image Processing Techniques, Patient Data Security, Personalized Medicine, Privacy Preservation Techniques, Quantum-Safe Encryption, Sensitive Health Data

About the Authors

Manisha Guduri is a tenure-track Assistant Professor at the Department of Electrical and Computer Engineering, Lawrence Technological University, USA. She is the author/coauthor of more than 74 research papers, holds three patent grants, and is a Senior Member of IEEE. She is Associate Editor-Digital Media of IEEE JETCAS for 2026 and Associate Editor for IEEE Transactions on Industrial Informatics.

George Pappas is an Associate Professor in the Department of Electrical and Computer Engineering (ECE) and 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, encryption and optimization algorithms, and the security of electronic medical data transfer.

Sandeep Thota is a Senior Member of Technical Staff at Oracle with over 7 years of experience in software engineering and healthcare IT innovation, currently leading development of the Semantic Index, a data stack enhancing Electronic Health Records with AI and ML. He is an IEEE Senior Member and holds patented solutions in the field.

Table of Contents

1. Privacy-Preserving Deep Learning for Healthcare Using Homomorphic Encryption
2. Patient Data Security Using Blockchain
3. Securing Healthcare Data With Quantum-Safe Encryption
4. Integrated Multimodal Redaction Architecture With Reversible Cryptographic Obfuscation
5. Federated Learning for Privacy-Preserving Healthcare Wearable Security
6. Privacy-Aware Federated Learning Architectures for Cross-Institutional Healthcare Collaboration
7. Privacy-Preserving Federated Learning for Multi-Hospital Patient Data Integration
8. Federated Learning and Collaborative AI in Medical Diagnostics: A Conceptual and Literature-Based Study
9. Managing Sensitive Health Data Through Federated Learning and Generative AI: Advanced Privacy-Preserving Techniques for Secure Digital Healthcare
10. Federated Generative Frameworks for Adaptive Privacy Preservation in Healthcare Systems
11. Secure Health Data Management via Federated Learning and Privacy-Aware Large Language Models
12. Leveraging Generative AI for Privacy-Preserving Synthetic Data Generation in Healthcare
13. Managing Sensitive Health Data Through Federated Learning and Generative AI Privacy Preserving Techniques: Conversational AI for Patient Engagement Transforming the Future of Healthcare Communication
14. Federated Learning for Healthcare Data Privacy and Security: From EHRs to Personalized Medicine
15. Futuristic Image Processing Techniques to Ameliorate Data Security and Privacy in Kidney Health Studies

Why buy this book?

Healthcare institutions worldwide face a hard trade-off: unlock the clinical and research value of patient data, or protect it. This volume gives libraries and practitioners a single, current reference on the two technologies actually resolving that trade-off in production — federated learning and generative AI — with 15 peer-reviewed chapters spanning blockchain, quantum-safe encryption, and multi-hospital collaboration. For academic medical libraries, it fills a fast-moving gap in the collection with material published in 2026 rather than a reprint of pre-pandemic data governance frameworks. For working data scientists and IT security leads, the case studies and architectures offer implementable starting points rather than purely theoretical treatment.

Keywords

federated learning, generative AI in healthcare, health data privacy, quantum-safe encryption, patient data security, blockchain healthcare, synthetic health data, HIPAA compliance technology

Target Audience

Healthcare professionals, academic medical libraries, data scientists, graduate and doctoral students, healthcare IT security teams, policymakers

Genre

Generative AI in Healthcare, Health Data Privacy

Q&A

What is federated learning in healthcare, and why does it matter for patient privacy?
Federated learning allows multiple hospitals or research centers to train a shared AI model collaboratively without any institution moving raw patient data off-site — only model updates are exchanged, not the underlying records.

How does generative AI help protect sensitive health data?
Generative AI can create realistic synthetic patient datasets that preserve the statistical properties needed for research and model training while containing no real patient information, reducing exposure risk.

Does this book cover regulatory compliance such as HIPAA or GDPR?
Yes — chapters address regulatory compliance considerations alongside the technical frameworks, including quantum-safe encryption and blockchain-based approaches relevant to compliance planning.

Who should read this book?
Healthcare professionals, data scientists, IT security teams, academic medical libraries, and graduate/doctoral students working on healthcare data infrastructure or health AI research.

Where can I buy Managing Sensitive Health Data Through Federated Learning and Generative AI?
This title is available directly from CLNZ Books, with worldwide shipping and secure payment.

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