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Decision Trees and Random Forest for Privacy-Preserving Data Mining

Decision Trees and Random Forest for Privacy-Preserving Data Mining

Gábor Szucs
Copyright: © 2013 |Pages: 20
ISBN13: 9781466641815|ISBN10: 1466641819|EISBN13: 9781466641822
DOI: 10.4018/978-1-4666-4181-5.ch004
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MLA

Szucs, Gábor. "Decision Trees and Random Forest for Privacy-Preserving Data Mining." Research and Development in E-Business through Service-Oriented Solutions, edited by Katalin Tarnay, et al., IGI Global, 2013, pp. 71-90. https://doi.org/10.4018/978-1-4666-4181-5.ch004

APA

Szucs, G. (2013). Decision Trees and Random Forest for Privacy-Preserving Data Mining. In K. Tarnay, S. Imre, & L. Xu (Eds.), Research and Development in E-Business through Service-Oriented Solutions (pp. 71-90). IGI Global. https://doi.org/10.4018/978-1-4666-4181-5.ch004

Chicago

Szucs, Gábor. "Decision Trees and Random Forest for Privacy-Preserving Data Mining." In Research and Development in E-Business through Service-Oriented Solutions, edited by Katalin Tarnay, Sandor Imre, and Lai Xu, 71-90. Hershey, PA: IGI Global, 2013. https://doi.org/10.4018/978-1-4666-4181-5.ch004

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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.

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