원문정보
보안공학연구지원센터(IJMUE)
International Journal of Multimedia and Ubiquitous Engineering
Vol.11 No.5
2016.05
pp.199-208
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
We establish a new model of privacy-preserving one-class support vector machine (SVM) based on vertically partitioned data. Every participant holds all the data with a part of attributes. They apply different random matrices to establish their own kernel matrix. By sharing these partial kernel matrices, we construct a global kernel matrix and establish linear and nonlinear privacy-preserving models. Experimental results on benchmark data sets verify the validity of the proposed models.
목차
Abstract
1. Introduction
2. One-Class SVM
3. Privacy-Preserving One-Class SVM
3.1 Linear Privacy-Preserving One-Class SVM
3.2 Privacy-Preserving Nonlinear One-Class SVM
4. Experiments
5. Conclusion
References
1. Introduction
2. One-Class SVM
3. Privacy-Preserving One-Class SVM
3.1 Linear Privacy-Preserving One-Class SVM
3.2 Privacy-Preserving Nonlinear One-Class SVM
4. Experiments
5. Conclusion
References
저자정보
참고문헌
자료제공 : 네이버학술정보