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Anonymization by Local Recoding in Data with Attribute Hierarchical Taxonomies

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4 Author(s)
Jiuyong Li ; University of South Australia, Adelaide ; Raymond Chi-Wing Wong ; Ada Wai-Chee Fu ; Jian Pei

Individual privacy will be at risk if a published data set is not properly deidentified. k-anonymity is a major technique to de-identify a data set. Among a number of k-anonymization schemes, local recoding methods are promising for minimizing the distortion of a k-anonymity view. This paper addresses two major issues in local recoding k-anonymization in attribute hierarchical taxonomies. First, we define a proper distance metric to achieve local recoding generalization with small distortion. Second, we propose a means to control the inconsistency of attribute domains in a generalized 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 anonymization method, Incognito, and a multidimensional recoding anonymization method, Multi. The proposed inconsistency handling method is able to balance distortion and consistency of a generalized view.

Published in:

IEEE Transactions on Knowledge and Data Engineering  (Volume:20 ,  Issue: 9 )