  K-Anonymity is a property for the measurement, management, and governance of
the data anonymization. Many implementations of k-anonymity have been described
in state of the art, but most of them are not able to work with a large number
of attributes in a "Big" dataset, i.e., a dataset drawn from Big Data. To
address this significant shortcoming, we introduce and evaluate \textsc{KGen}
an approach to K-anonymity featuring Genetic Algorithms. \textsc{KGen} promotes
such a meta-heuristic approach since it can solve the problem by finding a
pseudo-optimal solution in a reasonable time over a considerable load of input.
\textsc{KGen} allows the data manager to guarantee a high anonymity level while
preserving the usability and preventing loss of information entropy over the
data. Differently from other approaches that provide optimal global solutions
catered for small datasets, \textsc{KGen} works properly also over Big datasets
while still providing a good-enough solution. Evaluation results show how our
approach can still work efficiently on a real world dataset, provided by Dutch
Tax Authority, with 47 attributes (i.e., the columns of the dataset to be
anonymized) and over 1.5K+ observations (i.e., the rows of that dataset), as
well as on a dataset with 97 attributes and over 3942 observations.
