The authors proposed an efficient privacy preservation technique in classification. Classification is a mechanism for classifying testing data with the training data. This paper converts the original sample data sets into a group of unreal data sets, from which the original samples cannot be reconstructed without the entire group of unreal data sets. Meanwhile, an accurate decision tree can be built directly from those unreal data sets. Finally, the results yield accurate results even though additional samples added to the original datasets.