Data perturbation techniques are one of the most popular models for privacy preserving data mining [3, 1]. It is especially convenient for applications where the data owners need to export/publish the privacy-sensitive data. A data perturbation procedure can be simply described as follows. Before the data owner publishes the data, they randomly change the data in certain way to disguise the sensitive information while preserving the particular data property that is critical for building the data models. Several perturbation techniques have been proposed recently, among which the most typical ones are randomization approach [3] and condensation approach [1].