Earticle

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

키워드

저자

• Qiang Lin [ College of science, China Agricultural University, Beijing, 100083, China ]
• Huimin Pei [ College of science, China Agricultural University, Beijing, 100083, China ]
• Kuaini Wang [ College of science, China Agricultural University, Beijing, 100083, China ]
• Ping Zhong [ College of science, China Agricultural University, Beijing, 100083, China ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605