  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.
