Privacy Preserving Support Vector Machine Classification in WSN - Couverture souple

Azim, Muhammad Anwarul ; Kim, Dong Seong ; Park, Jong Sou

9786139846603: Privacy Preserving Support Vector Machine Classification in WSN

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ISBN 10 : 6139846609 ISBN 13 : 9786139846603

Editeur : LAP Lambert Academic Publishing, 2018

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�diteur: LAP Lambert Academic Publishing
Date d'�dition: 2018
Langue: anglais
ISBN 10: 6139846609
ISBN 13: 9786139846603
Reliure: Broch�
Nombre de pages: 60
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Privacy Preserving Support Vector Machine Classification in WSN

Muhammad Anwarul Azim

Edit� par LAP LAMBERT Academic Publishing Mai 2018, 2018

ISBN 10 : 6139846609 ISBN 13 : 9786139846603

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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The increasing prominence of Wireless Sensor Network (WSN) is stimulating greater interest in developing many application areas. WSNs promise viable solutions aiming at many monitoring problems despite energy, communication, computation & storage constraints. The security issues, data privacy, confidentiality and integrity become vital when the sensors are deployed in a hostile environment. Support Vector Machines (SVM) classification is one of the most widely used classifications having advantage of accuracy and sparse representation that SVMs provide for decision boundaries. It is important to achieve energy efficient data mining in WSN while preserving privacy of data. In this thesis we introduce SVM classification for WSN consisting energy efficiency advantage by distributed incremental learning for the training and construction of global SVM classification model without disclosing the data to others. We show security analysis and energy estimation for preserving privacy and energy efficiency in WSN using SVM. 60 pp. Englisch. N� de r�f. du vendeur 9786139846603

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Privacy Preserving Support Vector Machine Classification in WSN

Muhammad Anwarul Azim

Edit� par LAP Lambert Academic Publishing, 2018

ISBN 10 : 6139846609 ISBN 13 : 9786139846603

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Privacy Preserving Support Vector Machine Classification in WSN

Muhammad Anwarul Azim

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Privacy Preserving Support Vector Machine Classification in WSN

Muhammad Anwarul Azim

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Privacy Preserving Support Vector Machine Classification in WSN

Muhammad Anwarul Azim|Dong Seong Kim|Jong Sou Park

Edit� par LAP LAMBERT Academic Publishing, 2018

ISBN 10 : 6139846609 ISBN 13 : 9786139846603

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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Azim Muhammad AnwarulMuhammad Anwarul Azim is an Associate Professor in Computer Science & Engineering Department, University of Chittagong, Chittagong-4331, Bangladesh. His B.Sc (Engg.) degree is from Computer Science & Engineering . N� de r�f. du vendeur 385874087

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Privacy Preserving Support Vector Machine Classification in WSN

Muhammad Anwarul Azim

Edit� par LAP LAMBERT Academic Publishing Mai 2018, 2018

ISBN 10 : 6139846609 ISBN 13 : 9786139846603

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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -The increasing prominence of Wireless Sensor Network (WSN) is stimulating greater interest in developing many application areas. WSNs promise viable solutions aiming at many monitoring problems despite energy, communication, computation & storage constraints. The security issues, data privacy, confidentiality and integrity become vital when the sensors are deployed in a hostile environment. Support Vector Machines (SVM) classification is one of the most widely used classifications having advantage of accuracy and sparse representation that SVMs provide for decision boundaries. It is important to achieve energy efficient data mining in WSN while preserving privacy of data. In this thesis we introduce SVM classification for WSN consisting energy efficiency advantage by distributed incremental learning for the training and construction of global SVM classification model without disclosing the data to others. We show security analysis and energy estimation for preserving privacy and energy efficiency in WSN using SVM.VDM Verlag, Dudweiler Landstra�e 99, 66123 Saarbr�cken 60 pp. Englisch. N� de r�f. du vendeur 9786139846603

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Privacy Preserving Support Vector Machine Classification in WSN

Muhammad Anwarul Azim

Edit� par LAP LAMBERT Academic Publishing, 2018

ISBN 10 : 6139846609 ISBN 13 : 9786139846603

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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The increasing prominence of Wireless Sensor Network (WSN) is stimulating greater interest in developing many application areas. WSNs promise viable solutions aiming at many monitoring problems despite energy, communication, computation & storage constraints. The security issues, data privacy, confidentiality and integrity become vital when the sensors are deployed in a hostile environment. Support Vector Machines (SVM) classification is one of the most widely used classifications having advantage of accuracy and sparse representation that SVMs provide for decision boundaries. It is important to achieve energy efficient data mining in WSN while preserving privacy of data. In this thesis we introduce SVM classification for WSN consisting energy efficiency advantage by distributed incremental learning for the training and construction of global SVM classification model without disclosing the data to others. We show security analysis and energy estimation for preserving privacy and energy efficiency in WSN using SVM. N� de r�f. du vendeur 9786139846603

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