Enhanced p-sensitive k-anonymity models for privacy preserving data publishing
Article
Article Title | Enhanced p-sensitive k-anonymity models for privacy preserving data publishing |
---|---|
Article Category | Article |
Authors | Sun, Xiaoxun (Author), Wang, Hua (Author), Li, Jiuyong (Author) and Truta, Traian Marius (Author) |
Journal Title | Transactions on Data Privacy |
Journal Citation | 1 (2), pp. 53-66 |
Number of Pages | 14 |
Year | 2008 |
Place of Publication | Madrid, Spain |
Web Address (URL) | http://www.tdp.cat/issues/tdp.a001a08.pdf |
Abstract | Publishing data for analysis from a micro data table containing sensitive attributes, while maintaining individual privacy, is a problem of increasing significance today. The k-anonymity model was proposed for privacy preserving data publication. While focusing on identity disclosure, k-anonymity model fails to protect attribute disclosure to some extent. Many efforts are made to enhance the k-anonymity model recently. In this paper, we propose two new privacy protectionmodels called (p, )-sensitive k-anonymity and (p+, )-sensitive k-anonymity, respectively. Different from previous the p-sensitive k-anonymity model, these new introduced models allow us to release a lot more information without compromising privacy. Moreover, we prove that the (p, )-sensitive and (p+, )-sensitive k-anonymity problems are NP-hard. We also include testing and heuristic generating algorithms to generate desired micro data table. Experimental results show that our introduced model could significantly reduce the privacy breach. |
Keywords | k-anonymity models; privacy; data publishing |
ANZSRC Field of Research 2020 | 461399. Theory of computation not elsewhere classified |
460401. Cryptography | |
460499. Cybersecurity and privacy not elsewhere classified | |
Public Notes | File reproduced in accordance with the copyright policy of the publisher/author. |
Byline Affiliations | Department of Mathematics and Computing |
University of South Australia | |
Northern Kentucky University, United States |
https://research.usq.edu.au/item/9z3y4/enhanced-p-sensitive-k-anonymity-models-for-privacy-preserving-data-publishing
Download files
2197
total views432
total downloads2
views this month1
downloads this month