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• W2068643108 abstract "Privacy preservation is an important issue in the release of data for mining purposes. Recently, a novel l-diversity privacy model was proposed. However, even an l-diverse data set may have some severe problems leading to the revelation of individual sensitive information. In this paper, we remedy the problem by introducing distinct (l,α)-diversity, which, intuitively, demands that the total weight of the sensitive values in a given QI-group is at least α, where the weight is controlled by a pre-defined recursive metric system. We provide a thorough analysis of the distinct (l,α)-diversity and prove that the optimal distinct (l,α)-diversity problem with its two variants entropy (l,α)-diversity and recursive (c,l,α)-diversity are NP-hard, and propose a top-down anonymization approach to solve the distinct (l,α)-diversity problem with its variants. We show in the extensive experimental evaluations that the proposed methods are practical in terms of utility measurements and can be implemented efficiently." @default.
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• W2068643108 date "2011-03-01" @default.
• W2068643108 modified "2023-10-17" @default.
• W2068643108 title "A family of enhanced -diversity models for privacy preserving data publishing" @default.
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• W2068643108 doi "https://doi.org/10.1016/j.future.2010.07.007" @default.
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