Welcome to the InfoSci Platform
A State Decision Tree based Backtracking Algorithm for Multi-Sensitive Attribute Privacy Preserving

A State Decision Tree based Backtracking Algorithm for Multi-Sensitive Attribute Privacy Preserving

Yanchao Zhang, Qing Liu, JunJun Cheng, JiJia Yang
Copyright: © 2016 |Volume: 8 |Issue: 2 |Pages: 11
ISSN: 1941-8663|EISSN: 1941-8671|EISBN13: 9781466690615|DOI: 10.4018/IJITN.2016040101
Cite Article Cite Article

MLA

Zhang, Yanchao, et al. "A State Decision Tree based Backtracking Algorithm for Multi-Sensitive Attribute Privacy Preserving." IJITN vol.8, no.2 2016: pp.1-11. http://doi.org/10.4018/IJITN.2016040101

APA

Zhang, Y., Liu, Q., Cheng, J., & Yang, J. (2016). A State Decision Tree based Backtracking Algorithm for Multi-Sensitive Attribute Privacy Preserving. International Journal of Interdisciplinary Telecommunications and Networking (IJITN), 8(2), 1-11. http://doi.org/10.4018/IJITN.2016040101

Chicago

Zhang, Yanchao, et al. "A State Decision Tree based Backtracking Algorithm for Multi-Sensitive Attribute Privacy Preserving," International Journal of Interdisciplinary Telecommunications and Networking (IJITN) 8, no.2: 1-11. http://doi.org/10.4018/IJITN.2016040101

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

Beyond l-diversity model, an algorithm (l-BDT) based on state decision tree is proposed in this paper, which aims at protecting multi-sensitive attributes from being attacked. The algorithm considers the whole situations in equivalence partitioning for the first, prunes the decision tree according to some conditions for the second, and screens out the method with the least information loss of equivalence partitioning for the last. The analysis and experiments show that the l-BDT algorithm has the best performance in controlling the information loss. It can be ensured that the published data is the most closed towards the original data, so as to ensure that the published data is as useful as possible.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.