Privacy preservation for associative classification: an approximation algorithm
International Journal of Business Intelligence and Data Mining
DaWaK'12 Proceedings of the 14th international conference on Data Warehousing and Knowledge Discovery
Incremental processing and indexing for k, e-anonymisation
International Journal of Information and Computer Security
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Privacy protection is a major concern when microdata is released for ad hoc analyses. Anonymization schemes have to guarantee privacy goals, as well as preserve sufficient information to support reasonably accurate answers to ad hoc queries. In this paper, we focus on the case when the sensitive attributes are numerical (e.g., salary) for which $(k,e)$-anonymity was shown to be an appropriate privacy goal. We develop efficient algorithms for two optimization criteria for $(k,e)$-anonymity schemes, significantly improving on previous results. We evaluate our methods on a large real dataset, and show that they are scalable and accurate.