Privacy Preserving Support Vector Machine Classification in WSN: Second Edition - Softcover

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

Softcover

ISBN 10: 6209193501 ISBN 13: 9786209193507

Verlag: LAP LAMBERT Academic Publishing, 2025

Inhaltsangabe

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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Über die Autorin bzw. den Autor

Muhammad 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 Department, Shahjalal University of Science & Technology, Sylhet, Bangladesh and MSc (Engg.) from Korea Aerospace University, South Korea.

Details zur Ausgabe

• Verlag: LAP LAMBERT Academic Publishing
• Erscheinungsdatum: 2025
• Sprache: Englisch
• ISBN 10: 6209193501
• ISBN 13: 9786209193507
• Einband: Taschenbuch
• Anzahl der Seiten: 60
• Kontakt zum Hersteller: preigu GmbH & Co. KG; mail@preigu.de; ; Lengericher Landstr. 19; Osnabrück; Niedersachsen; 49078; Deutschland