Many applications employing the data mining techniques involve mining the data that includes private and sensitive information about the subjects. K-anonymity is a property that models the protection of released data against possible re-identification of the respondents to which the data refers. One of the interesting aspects of k-anonymity is its association with protection techniques that preserve the truthfulness of the data. It is however evident that the collection and analysis of data that include personal information may violate the privacy of the individuals to whom information refers. To guarantee the k-anonymity requirement, k-anonymity requires each quasi-identifier value in the released table to have at least k occurrences. In this paper, we present a survey of recent approaches that have been applied to the k-Anonymity problem.
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