Towards a more reasonable generalization cost metric for k-anonymization

Authors:
Zude Li;Guoqiang Zhan;Xiaojun Ye
Affiliations:
Institute of Information System and Engineering School of Software, Tsinghua University, Beijing, China;Institute of Information System and Engineering School of Software, Tsinghua University, Beijing, China;Institute of Information System and Engineering School of Software, Tsinghua University, Beijing, China
Venue:
BNCOD'06 Proceedings of the 23rd British National Conference on Databases, conference on Flexible and Efficient Information Handling
Year:
2006

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Abstract

A k-anonymity model contains an anonymity cost metric mechanism, which is critical for the whole k-anonymization process. The existing metrics cannot sufficiently identify the real cost on tabular microdata anonymization. We define a new cost metric that can be used for k-anonymization with the data generalization approach. The metric is more reasonable than the existing ones as it considers generalization range and range ratio rather than generalization height or height ratio, and the contribution of an attribute to the whole tuple rather than the amount of suppression cells. It can be used in most k-anonymity models for computing more precise anonymity costs.