IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Decision Trees and Random Forest for Privacy-Preserving Data Mining

Decision Trees and Random Forest for Privacy-Preserving Data Mining
View Sample PDF
Author(s): Gábor Szucs (Budapest University of Technology and Economics, Hungary)
Copyright: 2013
Pages: 20
Source title: Research and Development in E-Business through Service-Oriented Solutions
Source Author(s)/Editor(s): Katalin Tarnay (University of Pannonia, Hungary & Budapest University of Technology and Economics, Hungary), Sandor Imre (Budapest University of Technology and Economics, Hungary)and Lai Xu (Bournemouth University, UK)
DOI: 10.4018/978-1-4666-4181-5.ch004

Purchase

View Decision Trees and Random Forest for Privacy-Preserving Data Mining on the publisher's website for pricing and purchasing information.

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.

Related Content

Emrah Arğın. © 2022. 16 pages.
Ebru Gülbuğ Erol, Mustafa Gülsün. © 2022. 17 pages.
Yeşim Şener. © 2022. 18 pages.
Salim Kurnaz, Deimantė Žilinskienė. © 2022. 20 pages.
Dorothea Maria Bowyer, Walid El Hamad, Ciorstan Smark, Greg Evan Jones, Claire Beattie, Ying Deng. © 2022. 29 pages.
Savas S. Ates, Vildan Durmaz. © 2022. 24 pages.
Nusret Erceylan, Gaye Atilla. © 2022. 20 pages.
Body Bottom