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Issue No. 09 - September (2008 vol. 20)
ISSN: 1041-4347
pp: 1181-1194
Jiuyong Li , University of South Australia, Adelaide
Raymond Chi-Wing Wong , the Chinese University of Hong Kong, Hong Kong
Ada Wai-Chee Fu , The Chinese University of Hong Kong, Hong Kong
Jian Pei , Simon Fraser Univeristy, Burnaby
Individual privacy will be at risk if a published data set is not properly de-identified. k-anonymity is a major technique to de-identify a data set. Among a number of k-anonymisation schemes, local recoding methods are promising for minimising the distortion of a k-anonymity view. This paper addresses two major issues in local recoding k-anonymisation in attribute hierarchical taxonomies. Firstly, we define a proper distance metric to achieve local recoding generalisation with small distortion. Secondly, we propose a means to control the inconsistency of attribute domains in a generalised view by local recoding. We show experimentally that our proposed local recoding method based on the proposed distance metric produces higher quality k-anonymity tables in three quality measures than a global recoding anonymisation method, Incognito, and a multidimensional recoding anonymisation method, Multi. The proposed inconsistency handling method is able to balance distortion and consistency of a generalised view.
Security and Privacy Protection, Data mining

A. W. Fu, J. Li, R. C. Wong and J. Pei, "Anonymization by Local Recoding in Data with Attribute Hierarchical Taxonomies," in IEEE Transactions on Knowledge & Data Engineering, vol. 20, no. , pp. 1181-1194, 2008.
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