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Privacy Preserving Support Vector Machine Classification in WSN

By Azim, Muhammad Anwarul

Privacy Preserving Support Vector Machine Classification in WSN

By Azim, Muhammad Anwarul

0 - Default Title
Description
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.
Product details
Binding:
Paperback
Number of Pages:
60
Release Date:
2025-11-03
Publication Date:
2025-11-03
Publisher:
LAP LAMBERT Academic Publishing
Languages:
Original: English
ISBN10:
6209193501
ISBN13:
9786209193507
Weight:
107 g
Height:
150 cm
Width:
220 cm
Thickness:
4 cm
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