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.
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