(p+, α)-sensitive k-anonymity: a new enhanced privacy protection model
Paper
| Paper/Presentation Title | (p+, α)-sensitive k-anonymity: a new enhanced privacy protection model |
|---|---|
| Presentation Type | Paper |
| Authors | Sun, Xiaoxun (Author), Wang, Hua (Author), Truta, Traian Marius (Author), Li, Jiuyong (Author) and Li, Ping (Author) |
| Editors | Wu, Qiang |
| Journal or Proceedings Title | Proceedings of the 8th IEEE International Conference on Computer and Information Technology |
| Number of Pages | 6 |
| Year | 2008 |
| Place of Publication | United States |
| ISBN | 9781424423583 |
| Digital Object Identifier (DOI) | https://doi.org/10.1109/CIT.2008.4594650 |
| Web Address (URL) of Paper | https://ieeexplore.ieee.org/document/4594650 |
| Conference/Event | 8th IEEE International Conference on Computer and Information Technology |
| Event Details | 8th IEEE International Conference on Computer and Information Technology Event Date 08 to end of 11 Jul 2008 Event Location Sydney, Australia |
| Abstract | Publishing data for analysis from a microdata 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 kanonymity model recently. In this paper, we propose a new privacy protection model called (p+, α)-sensitive kanonymity, where sensitive attributes are first partitioned into categories by their sensitivity, and then the categories that sensitive attributes belong to are published. Different from previous enhanced k-anonymity models, this model allows us to release a lot more information without compromising privacy. We also provide testing and heuristic generating algorithms. Experimental results show that our introduced model could significantly reduce the privacy breach. |
| Keywords | k-anonymity models; privacy protection models; (p+, α)-sensitive k-anonymity model |
| ANZSRC Field of Research 2020 | 460599. Data management and data science not elsewhere classified |
| 460499. Cybersecurity and privacy not elsewhere classified | |
| 469999. Other information and computing sciences not elsewhere classified | |
| Public Notes | File reproduced in accordance with the copyright policy of the publisher/author. |
| Byline Affiliations | Department of Mathematics and Computing |
| Northern Kentucky University, United States | |
| University of South Australia | |
| Beihang University, China |
https://research.usq.edu.au/item/9yv83/-p-sensitive-k-anonymity-a-new-enhanced-privacy-protection-model
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