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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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Acerca del autor
Dr. Manisha Guduri is currently an Assistant Professor (Tenure Track) at Lawrence Technological University. She is the author/ coauthor of more than 80 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 received two patent grants. She is a senior member of IEEE, USA. She is also a member of various IEEE Societies - IEEE YP, IEEE WiE, Circuits and Systems, Computer Society, Sensor Council, etc. She is IEEE Lafayette Section Chair for 2025. She is an IEEE USA Awards and Recognition Committee Member at Large. She is nominated for the Associate Editor - DM role of IEEE JETCAS for 2026. 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.
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Managing Sensitive Health Data Through Federated Learning and Generative Ai: Privacy Preserving Techniques
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Managing Sensitive Health Data Through Federated Learning and Generative Ai: Privacy Preserving Techniques
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Managing Sensitive Health Data Through Federated Learning and Generative AI (Hardcover)
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Hardcover. Condición: new. Hardcover. 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 UK warehouse or from our Australian or US warehouses, depending on stock availability. Nº de ref. del artículo: 9798337374260
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Managing Sensitive Health Data Through Federated Learning and Generative AI (Hardcover)
Impresión bajo demandaLibrería: Grand Eagle Retail, Bensenville, IL, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Hardcover. Condición: new. Hardcover. 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 multiple locations in the US or from the UK, depending on stock availability. Nº de ref. del artículo: 9798337374260
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Managing Sensitive Health Data Through Federated Learning and Generative AI (Hardcover)
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Calificación del vendedor: 5 de 5 estrellas
Hardcover. Condición: new. Hardcover. 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. Nº de ref. del artículo: 9798337374260
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