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Privacy-Preserving Collaborative Prediction using Random Forests

Publication ,  Journal Article
Giacomelli, I; Jha, S; Kleiman, R; Page, D; Yoon, K
November 21, 2018

We study the problem of privacy-preserving machine learning (PPML) for ensemble methods, focusing our effort on random forests. In collaborative analysis, PPML attempts to solve the conflict between the need for data sharing and privacy. This is especially important in privacy sensitive applications such as learning predictive models for clinical decision support from EHR data from different clinics, where each clinic has a responsibility for its patients' privacy. We propose a new approach for ensemble methods: each entity learns a model, from its own data, and then when a client asks the prediction for a new private instance, the answers from all the locally trained models are used to compute the prediction in such a way that no extra information is revealed. We implement this approach for random forests and we demonstrate its high efficiency and potential accuracy benefit via experiments on real-world datasets, including actual EHR data.

Duke Scholars

Publication Date

November 21, 2018
 

Citation

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Giacomelli, I., Jha, S., Kleiman, R., Page, D., & Yoon, K. (2018). Privacy-Preserving Collaborative Prediction using Random Forests.
Giacomelli, Irene, Somesh Jha, Ross Kleiman, David Page, and Kyonghwan Yoon. “Privacy-Preserving Collaborative Prediction using Random Forests,” November 21, 2018.
Giacomelli I, Jha S, Kleiman R, Page D, Yoon K. Privacy-Preserving Collaborative Prediction using Random Forests. 2018 Nov 21;
Giacomelli I, Jha S, Kleiman R, Page D, Yoon K. Privacy-Preserving Collaborative Prediction using Random Forests. 2018 Nov 21;

Publication Date

November 21, 2018