A Privacy Preserving Approach Based on Attributes Correlation Partition for Multiple Sensitive Attributes

In recent years,l-diversity models are suitable not only for single sensitive attribute data tables,but also for multiple sensitive attributes data tables. However,most of the research is based on lossy join,it breaks the relationship between data. To address these problems,a model based on multiple sensitive attributes is proposed. The main idea of the model is that it proposes a l-maximum principle that can satisfy the multiple sensitive attributes l-diversity at first. Then,to protect the relationship between data,the model partitions attributes by the dependency degree between attributes. Finally,a multiple sensitive attributes l-maximum algorithm( MSA l-maximum) is proposed. The experiment results show that the proposed model can preserve the security of sensitive data,meanwhile it can also reduce the information hidden rate and keep a high data utility.