Hiding Classification Rules for Data Sharing with Privacy Preservation

In this paper, we propose a method of hiding sensitive classification rules from data mining algorithms for categorical datasets. Our approach is to reconstruct a dataset according to the classification rules that have been checked and agreed by the data owner for releasing to data sharing. Unlike the other heuristic modification approaches, firstly, our method classifies a given dataset. Subsequently, a set of classification rules is shown to the data owner to identify the sensitive rules that should be hidden. After that we build a new decision tree that is constituted only non-sensitive rules. Finally, a new dataset is reconstructed. Our experiments show that the sensitive rules can be hidden completely on the reconstructed datasets. While non-sensitive rules are still able to discovered without any side effect. Moreover, our method can also preserve high usability of reconstructed datasets.