Decision Trees and Random Forest for Privacy-Preserving Data Mining

Abstract

The objective of this chapter is to present brief literature and new results of research in privacy-preserving data mining as an important privacy issue in the e-business area. The chapter focuses on classification problems in business analytics, where the enterprises can gain large profit using predicted results by classification. The decision tree is a well-known classification technique, and its modification by the Randomized Response technique is described for privacy-preserving data mining. This algorithm is developed for all types of attributes. The largest contribution of this chapter is a new method, so called Random Response Forest, consisting of many decision trees and a randomization technique. Random Response Forest is similar to Random Forest, but it is able to solve privacy problems. This consists of many shallow trees, where a shallow tree is a special decision tree with a Randomized Response technique, and the precision of Random Response Forest is better than at a tree.