Privacy preserving techniques for decision trees

Abstract

As a representative classification model, decision tree has been extensively applied in data mining. It generates a series of if-then rules based on the homogeneity of class distribution. In a society where data spreads everywhere for knowledge discovery, the privacy of the data respondents is likely to be leaked and abused. Based on this concern, we propose an overview of the rapidly evolving research results focusing on privacy preserving decision tree induction. The research results are summarised according to the characteristics of related privacy preservation techniques, which include data perturbation, cryptography, and data anonymisation. In addition, we demonstrate the comparison between the merits and demerits of these methods considering the specific property of decision tree induction. At last, we conclude the future trend of privacy preserving techniques.