Random Response Forest for Privacy-Preserving Classification. (14th November 2013)

• Record Type: Journal Article
• Authors: Szűcs, Gábor
• Other Names: Nicolet André Academic Editor.
• Is Part Of: Journal of computational engineering. Volume 2013(2013)
• Publication Date: 2013-11-14
• Subjects: Engineering mathematics -- Periodicals; Engineering -- Mathematical models -- Periodicals; Engineering -- Mathematical models; Engineering mathematics; Periodicals; 620.00285
• DOI: 10.1155/2013/397096
• Languages: English
• ISSNs: 2356-7260

Abstract

Abstract : 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.