文件名称:eserv
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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.
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.
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eserv.pdf