Anonymization by Local Recoding in Data with Attribute Hierarchical Taxonomies

Abstract--Individual privacy will be at risk if a published data set is not properly deidentified. k-Anonymity is a major technique to deidentify 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...

• Type: Journal
• Year: 2008
• Where: TKDE
• Authors: Jiuyong Li, Raymond Chi-Wing Wong, Ada Wai-Chee Fu, Jian Pei
• Keywords: Local Recoding, Local Recoding Method, Recoding Anonymization Method, TKDE 2008