Earticle

초록

영어

We propose a new algorithm of privacy-preserving one-class support vector machine (SVM) with horizontally partitioned data. Every participant holds a part of data with all the data attributes. They apply the same random matrix to establish their own kernel matrix. By sharing these partial kernel matrices, we generate a global kernel matrix and establish two privacy-preserving one-class SVM models, which include the linear model and the nonlinear model. Partial kernel matrix can protect the privacy of the participants, and the global kernel matrix can ensure the classification accuracy. Experimental results on benchmark data sets indicate the effectiveness of the proposed algorithms.

목차

Abstract
 1. Introduction
 2. One-Class SVM
 3. Horizontally Privacy-Preserving One-Class SVM
  3.1. Privacy-Preserving Linear One-Class SVM
  3.2. Privacy-Preserving Nonlinear One-Class SVM
 4. Experiments
 5. Conclusion
 Acknowledgments
 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, Xi’an Shiyou University, Xi’an 710065, China ]
• Ping Zhong [ College of Science, China Agricultural University, Beijing, 100083, China ] Corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605