Earticle

초록

영어

Differential privacy is a security guarantee model which widely used in privacy preserving data publishing, but the query result can’t be used in data research directly, especially in high-dimensional datasets. To address this problem, we propose a dimensionality reduction method. The core idea of this method is using a series of low-dimensional datasets to reconstruct a high-dimensional dataset, it improves data availability eventually. The main issue of this method is the reconstruction integrity, so a special sampling via set cover model is proposed in this article, which builds a multidimensional composite marginal tables set as a new middleware in differential privacy model. As a result, any form of disjunctive queries can be answered, and the accuracy of data query is improved. The experiment results also show the effectiveness of our method in practice.

목차

Abstract
 1. Introduction
 2. Related Work
 3. Problem Statement
 4. Algorithms
  4.1. Multi-stage Stratified Sampling Method
  4.2. Weighted Set Cover Model
  4.3. Consistency Processing
 5. Experiment
  5.1. Introduction of Data Set
  5.3. Comparison on Data Utility
  5.2. Comparison on Time Complexity
 6. Conclusion
 Acknowledgements
 References

키워드

저자

• Zhonglian Hu [ School of Information Science and Technology, Dalian Maritime University No. 1, Linghai Road, Dalian, P.R.China ]
• Zhaobin Liu [ School of Information Science and Technology, Dalian Maritime University No. 1, Linghai Road, Dalian, P.R.China ] Corresponding author
• Yangyang Xu [ School of Information Science and Technology, Dalian Maritime University No. 1, Linghai Road, Dalian, P.R.China ]
• Zhiyang Li [ School of Information Science and Technology, Dalian Maritime University No. 1, Linghai Road, Dalian, P.R.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