9789811637636 - privacy-preserving deep learning: a comprehensive survey di kim, kwangjo; tanuwidjaja, harry chandra (22 risultati)
Lingua: Inglese
Editore: Springer, 2021
Serie: Libro 9 di 10 - SpringerBriefs on Cyber Security Systems and Networks
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Paperback. Condizione: Brand New. 88 pages. 9.25x6.10x0.19 inches. In Stock.
Lingua: Inglese
Editore: Springer, 2021
Serie: Libro 9 di 10 - SpringerBriefs on Cyber Security Systems and Networks
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Taschenbuch. Condizione: Neu. Privacy-Preserving Deep Learning | A Comprehensive Survey | Kwangjo Kim (u. a.) | Taschenbuch | SpringerBriefs on Cyber Security Systems and Networks | xiv | Englisch | 2021 | Springer | EAN 9789811637636 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg,… juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Taschenbuch. Condizione: 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.
Lingua: Inglese
Editore: Springer Nature Singapore, 2021
Serie: Libro 9 di 10 - SpringerBriefs on Cyber Security Systems and Networks
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Condizione: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher | 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.
Lingua: Inglese
Editore: Springer Nature Singapore, 2021
Serie: Libro 9 di 10 - SpringerBriefs on Cyber Security Systems and Networks
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Da: Buchpark, Trebbin, GermaniaBuchpark
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Condizione: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | 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 crypt…ographic 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.
Lingua: Inglese
Editore: Springer Nature Singapore, 2021
Serie: Libro 9 di 10 - SpringerBriefs on Cyber Security Systems and Networks
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Softcover. Condizione: gut. 2021. Privacy-Preserving Deep Learning In deutscher Sprache. pages.
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Editore: Springer, 2021
Serie: Libro 9 di 10 - SpringerBriefs on Cyber Security Systems and Networks
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Condizione: Brand New. New. US edition. Print on demand title. Delivery takes 20-25 days. Excellent Customer Service.
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Editore: Springer Nature Singapore Jul 2021, 2021
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Taschenbuch. Condizione: 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 a…nd 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.
Lingua: Inglese
Editore: Springer, 2021
Serie: Libro 9 di 10 - SpringerBriefs on Cyber Security Systems and Networks
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Editore: Springer, Berlin|Springer Nature Singapore|Springer, 2021
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Condizione: 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 priva…cy-preserving and cryptographic protocol.
Lingua: Inglese
Editore: Springer, 2021
Serie: Libro 9 di 10 - SpringerBriefs on Cyber Security Systems and Networks
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Da: Biblios, frankfurt am main, HESSE, GermaniaBiblios
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Condizione: New. PRINT ON DEMAND.
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Editore: Springer, Springer Jul 2021, 2021
Serie: Libro 9 di 10 - SpringerBriefs on Cyber Security Systems and Networks
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. 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 c…ryptographic 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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 88 pp. Englisch.
