Differentially private attribute selection for classification

Selecting a relevant subset of attributes is one of the most important data preprocessing steps of data mining and machine learning solutions. For the classification task, selection is based on the correlation between an attribute and the class attribute. There are various studies on privacy preserving classification. However, there is no attribute selection solution for such work in the literature. In this study, novel attribute selection methods based on the state of the art solution in statistical database security, known as differential privacy, are proposed. The proposed solutions are implemented with the popular data mining library WEKA and experimental results confirm the positive effects of the proposed solutions on classification accuracy.