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.
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© 2006 Springer-Verlag Berlin Heidelberg
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Li, Z., Zhan, G., Ye, X. (2006). Towards a More Reasonable Generalization Cost Metric for K-Anonymization. In: Bell, D.A., Hong, J. (eds) Flexible and Efficient Information Handling. BNCOD 2006. Lecture Notes in Computer Science, vol 4042. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11788911_25
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DOI: https://doi.org/10.1007/11788911_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-35969-2
Online ISBN: 978-3-540-35971-5
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