Abstract. 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.