Earticle

초록

영어

This paper is based on the (α, k)-anonymity models by introducing the individualized privacy-sensitive factor then computing the individualized privacy preservation demand degree of each sensitive value to realize the individualized service of each sensitive value. It combining with the top-down local codes and the sensitive attribute generalization anonymous technology proposes a method based on the attribute classification tree to realize the individualized (α, k)- anonymity models. At last, the results of simulation show that this method could complete the anonymilization and meet the demand of individualized privacy preservation, and the information loss is lower than the original one.

목차

Abstract
 1. Introduction
 2. Related Work
 3. Definitions
 4. Individualized (α, k)-anonymous Model
 5. Experiment and Result Analysis
  5.1. Applicability Analysis
  5.2. Information Loss Analysis
  5.3. Algorithm Efficiency Analysis
 6. Conclusion
 Acknowledgements
 References

키워드

저자

• Song Yang [ College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China ]
• Li Lijie [ College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China ]
• Zhang Jianpei [ College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China ]
• Yang Jing [ College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Security and Its Applications

• 간기

  격월간

• pISSN

  1738-9976

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605