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
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