Abstract
A great challenge in privacy preservation is to trade off two important issues: data utility and privacy preservation, in publication of dataset which usually contains sensitive information. Anonymization is a well-represent approach to achieve this, and there exist several anonymity models. Most of those models mainly focuses on protecting privacy exerting identical protection for the whole table with pre-defined parameters. As a result, it could not meet the diverse requirements of protection degrees varied with different sensitive values.Motivated by this, this paper firstly introduces an a-diversity k-anonymity model (ADKAM) to satisfy the diversity deassociation for sensitive values, ant then designs a framework based on an improved microaggregation algorithm, as an alternative to generalization/ suppression to achieve anonymization. By using this framework, we improve the data utility and disclosure risk of privacy disclosure. We conduct several experiments to validate our schemes.
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Cheng, L., Cheng, S., Jiang, F. (2015). ADKAM: A-Diversity K-Anonymity Model via Microaggregation. In: Lopez, J., Wu, Y. (eds) Information Security Practice and Experience. ISPEC 2015. Lecture Notes in Computer Science(), vol 9065. Springer, Cham. https://doi.org/10.1007/978-3-319-17533-1_36
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DOI: https://doi.org/10.1007/978-3-319-17533-1_36
Publisher Name: Springer, Cham
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