Privacy Preserving Support Vector Machine Classification in WSN - Tapa blanda

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

 
9786139846603: Privacy Preserving Support Vector Machine Classification in WSN
Tapa blanda
ISBN 10: 6139846609 ISBN 13: 9786139846603
Editorial: LAP Lambert Academic Publishing, 2018

Sinopsis

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.

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Editorial
LAP Lambert Academic Publishing
Año de publicación
2018
Idioma
Inglés
ISBN 10 
6139846609
ISBN 13 
9786139846603
Encuadernación
Tapa blanda
Número de páginas
60
Contacto del fabricante
no disponible
Persona responsable
no disponible