An Efficient Model for Publishing Microdata with Multiple Sensitive Attributes
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Abstract
The purpose of this work is to propose an anonymization model. It is used to address privacy violation issues in datasets that have multiple sensitive attributes. To achieve privacy preservation constraints and maintain data utilities, the sensitive attributes of datasets are grouped to be nominal and continuous attributes. With the nominal sensitive attribute, the data utility and privacy are maintained by the confidence of data re-identification. With another data type, the continuous data, the data utility and privacy are maintained by the data bounding. The proposed model is evaluated by using extensive experiments. The experimental results indicate that the proposed model is more effective and efficient than the compared models. Moreover, the datasets satisfy the privacy preservation constraints of the proposed model, which can guarantee the confidence and bounding of data re-identification.
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