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| Content Provider | Springer Nature Link |
|---|---|
| Author | Wong, Raymond Li, Jiuyong Fu, Ada Wang, Ke |
| Copyright Year | 2008 |
| Abstract | Privacy preservation is an important issue in the release of data for mining purposes. The k-anonymity model has been introduced for protecting individual identification. Recent studies show that a more sophisticated model is necessary to protect the association of individuals to sensitive information. In this paper, we propose an (α, k)-anonymity model to protect both identifications and relationships to sensitive information in data. We discuss the properties of (α, k)-anonymity model. We prove that the optimal (α, k)-anonymity problem is NP-hard. We first present an optimal global-recoding method for the (α, k)-anonymity problem. Next we propose two scalable local-recoding algorithms which are both more scalable and result in less data distortion. The effectiveness and efficiency are shown by experiments. We also describe how the model can be extended to more general cases. |
| Starting Page | 209 |
| Ending Page | 234 |
| Page Count | 26 |
| File Format | |
| ISSN | 09259902 |
| Journal | Journal of Intelligent Information Systems |
| Volume Number | 33 |
| Issue Number | 2 |
| e-ISSN | 15737675 |
| Language | English |
| Publisher | Springer US |
| Publisher Date | 2009-01-08 |
| Publisher Place | Boston |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Privacy Data mining Anonymity Privacy preservation Data publishing Business Information Systems Document Preparation and Text Processing Artificial Intelligence (incl. Robotics) Data Structures, Cryptology and Information Theory |
| Content Type | Text |
| Resource Type | Article |
| Subject | Artificial Intelligence Computer Networks and Communications Information Systems Software Hardware and Architecture |
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