Table of Contents
Journal of Computational Engineering
Volume 2013 (2013), Article ID 397096, 6 pages
Research Article

Random Response Forest for Privacy-Preserving Classification

Department of Telecommunications and Media Informatics, BME, Hungary and Inter-University Centre for Telecommunications and Informatics, Kassai str, Debrecen 4028, Hungary

Received 23 April 2013; Revised 25 September 2013; Accepted 3 October 2013

Academic Editor: André Nicolet

Copyright © 2013 Gábor Szűcs. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The paper deals with classification in privacy-preserving data mining. An algorithm, the Random Response Forest, is introduced constructing many binary decision trees, as an extension of Random Forest for privacy-preserving problems. Random Response Forest uses the Random Response idea among the anonymization methods, which instead of generalization keeps the original data, but mixes them. An anonymity metric is defined for undistinguishability of two mixed sets of data. This metric, the binary anonymity, is investigated and taken into consideration for optimal coding of the binary variables. The accuracy of Random Response Forest is presented at the end of the paper.