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.
CITATION STYLE
Cheng, L., Cheng, S., & Jiang, F. (2015). ADKAM: A-diversity k-anonymity model via microaggregation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9065, pp. 533–547). Springer Verlag. https://doi.org/10.1007/978-3-319-17533-1_36
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