(alpha, k)-anonymity: an enhanced k-anonymity model for privacy-preserving data publishing
Date
2006
Authors
Wong, R.
Li, J.
Fu, A.W.C.
Wang, K.
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Conference paper
Citation
Proceedings of the twelfth ACMKDD international conference on knowledge discovery and data mining (KDD), 2006, pp.754-759
Statement of Responsibility
Conference Name
KDD '06 (20 Aug 2006 : Philadelphia, Pennsylvania)
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 globalrecoding method for the (α, 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.
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Dissertation Note
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Copyright 2006 ACM