Task Oriented Privacy Preserving Data Publishing Using Feature Selection.

In this work we show that feature selection can be used to preserve privacy of individuals without compromising the accuracy of data classification. Furthermore, when feature selection is combined with anonymization techniques, we are able to publish privacy preserving datasets. We use several UCI data sets to empirically support our claim. The obtained results show that these privacy-preserving datasets provide classification accuracy comparable and in some cases superior to the accuracy of classification of the original datasets. We generalized the results with a paired t-test applied on different levels of anonymization.