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.
