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Published by Springer, 2018
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Published by Springer, 2018
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Published by Springer, 2021
ISBN 10: 9811637636 ISBN 13: 9789811637636
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Published by Springer, 2021
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Published by Springer, 2018
ISBN 10: 9811314438 ISBN 13: 9789811314438
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Published by Springer, 2021
ISBN 10: 9811637636 ISBN 13: 9789811637636
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Published by Springer, 2021
ISBN 10: 9811637636 ISBN 13: 9789811637636
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Published by Springer-Verlag New York Inc, 2018
ISBN 10: 9811314438 ISBN 13: 9789811314438
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Published by Springer, 2021
ISBN 10: 9811637636 ISBN 13: 9789811637636
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Published by Springer, 2018
ISBN 10: 9811314438 ISBN 13: 9789811314438
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Published by Springer, 2021
ISBN 10: 9811637636 ISBN 13: 9789811637636
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ISBN 10: 9811637636 ISBN 13: 9789811637636
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Published by Springer, 2018
ISBN 10: 9811314438 ISBN 13: 9789811314438
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Published by Springer Nature Singapore Jul 2021, 2021
ISBN 10: 9811637636 ISBN 13: 9789811637636
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book discusses the state-of-the-art in privacy-preserving deep learning (PPDL), especially as a tool for machine learning as a service (MLaaS), which serves as an enabling technology by combining classical privacy-preserving and cryptographic protocols with deep learning. Google and Microsoft announced a major investment in PPDL in early 2019. This was followed by Google's infamous announcement of 'Private Join and Compute,' an open source PPDL tools based on secure multi-party computation (secure MPC) and homomorphic encryption (HE) in June of that year. One of the challenging issues concerning PPDL is selecting its practical applicability despite the gap between the theory and practice. In order to solve this problem, it has recently been proposed that in addition to classical privacy-preserving methods (HE, secure MPC, differential privacy, secure enclaves), new federated or split learning for PPDL should also be applied. This concept involves building a cloud framework that enables collaborative learning while keeping training data on client devices. This successfully preserves privacy and while allowing the framework to be implemented in the real world.This book provides fundamental insights into privacy-preserving and deep learning, offering a comprehensive overview of the state-of-the-art in PPDL methods. It discusses practical issues, and leveraging federated or split-learning-based PPDL. Covering the fundamental theory of PPDL, the pros and cons of current PPDL methods, and addressing the gap between theory and practice in the most recent approaches, it is a valuable reference resource for a general audience, undergraduate and graduate students, as well as practitioners interested learning about PPDL from the scratch, and researchers wanting to explore PPDL for their applications. 88 pp. Englisch.
Published by Springer, 2021
ISBN 10: 9811637636 ISBN 13: 9789811637636
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Published by Springer, 2018
ISBN 10: 9811314438 ISBN 13: 9789811314438
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Published by Springer-Nature New York Inc, 2021
ISBN 10: 9811637636 ISBN 13: 9789811637636
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Published by Springer Singapore, 2018
ISBN 10: 9811314438 ISBN 13: 9789811314438
Seller: moluna, Greven, Germany
Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Surveys deep learning-based IDSs Suggests future directions for IDS research Describes how to apply deep learning in IDSDiscusses learning for better attack detectionKwangjo Kim is a Fellow of the Interna.
Published by Springer, 2018
ISBN 10: 9811314438 ISBN 13: 9789811314438
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Published by Springer Nature Singapore, 2021
ISBN 10: 9811637636 ISBN 13: 9789811637636
Seller: AHA-BUCH GmbH, Einbeck, Germany
Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book discusses the state-of-the-art in privacy-preserving deep learning (PPDL), especially as a tool for machine learning as a service (MLaaS), which serves as an enabling technology by combining classical privacy-preserving and cryptographic protocols with deep learning. Google and Microsoft announced a major investment in PPDL in early 2019. This was followed by Google's infamous announcement of 'Private Join and Compute,' an open source PPDL tools based on secure multi-party computation (secure MPC) and homomorphic encryption (HE) in June of that year. One of the challenging issues concerning PPDL is selecting its practical applicability despite the gap between the theory and practice. In order to solve this problem, it has recently been proposed that in addition to classical privacy-preserving methods (HE, secure MPC, differential privacy, secure enclaves), new federated or split learning for PPDL should also be applied. This concept involves building a cloud framework that enables collaborative learning while keeping training data on client devices. This successfully preserves privacy and while allowing the framework to be implemented in the real world.This book provides fundamental insights into privacy-preserving and deep learning, offering a comprehensive overview of the state-of-the-art in PPDL methods. It discusses practical issues, and leveraging federated or split-learning-based PPDL. Covering the fundamental theory of PPDL, the pros and cons of current PPDL methods, and addressing the gap between theory and practice in the most recent approaches, it is a valuable reference resource for a general audience, undergraduate and graduate students, as well as practitioners interested learning about PPDL from the scratch, and researchers wanting to explore PPDL for their applications.
Published by Springer, Berlin|Springer Nature Singapore|Springer, 2021
ISBN 10: 9811637636 ISBN 13: 9789811637636
Seller: moluna, Greven, Germany
Kartoniert / Broschiert. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book discusses the state-of-the-art in privacy-preserving deep learning (PPDL), especially as a tool for machine learning as a service (MLaaS), which serves as an enabling technology by combining classical privacy-preserving and cryptographic protocol.