Privacy Preserving Support Vector Machines in Wireless Sensor Networks

Authors:
Dong Seong Kim;Muhammad Anwarul Azim;Jong Sou Park
Affiliations:
-;-;-
Venue:
ARES '08 Proceedings of the 2008 Third International Conference on Availability, Reliability and Security
Year:
2008

Citing 0
Cited 2

Privacy-preserving SVM classification on vertically partitioned data without secure multi-party computation

ICNC'09 Proceedings of the 5th international conference on Natural computation
Cloud-enabled privacy-preserving collaborative learning for mobile sensing

Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

It is important to achieve energy efficient data mining in Wireless Sensor Networks (WSN) while preserving privacy of data. In this paper, we present a privacy preserving data mining based on Support Vector Machines (SVM). We review the previous approach in privacy preserving data mining in distributed system. And we also review energy efficient data mining in WSN. We then propose an energy efficient privacy preserving data mining in WSN. We use SVM because it has been shown best classification accuracy and sparse data presentation using support vectors. We show security analysis and energy estimation of our proposed approach.