Wong, Raymond Chi-Wing and Li, Jiuyong and Fu, Ada Wai-Chee and Wang, Ke (2006) (alpha, k)-anonymity: an enhanced k-anonymity model for privacy preserving data publishing. In: 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'06), 20-23 Aug 2006, Philadelphia, USA.
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 (alpha, k)-anonymity model to protect both identifications and relationships to sensitive information in data. We discuss the properties of (alpha, k)-anonymity model. We prove that the optimal (alpha, k)- anonymity problem is NP-hard. We first present an optimal global recoding method for the (alpha, k)-anonymity problem. Next we propose a local-recoding algorithm which is 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.
|Item Type:||Conference or Workshop Item (Commonwealth Reporting Category E) (Poster)|
|Additional Information:||Author's version deposited in accordance with the copyright policy of the publisher. Copyright ACM 2006. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 20-23 August 2006, Philadelphia, USA.|
|Uncontrolled Keywords:||anonymity, privacy preservation, data publishing, data mining|
|Subjects:||280000 Information, Computing and Communication Sciences|
|Depositing User:||Dr Jiuyong (John) Li|
|Date Deposited:||11 Oct 2007 00:57|
|Last Modified:||02 Jul 2013 22:42|
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