Fully Homomorphic Training and Inference on Binary Decision Tree and Random Forest

ePrint Report: Fully Homomorphic Training and Inference on Binary Decision Tree and Random Forest

Hojune Shin, Jina Choi, Dain Lee, Kyoungok Kim, Younho Lee

This paper introduces a new method for training decision trees and random forests using CKKS homomorphic encryption (HE) in cloud environments, enhancing data privacy from multiple sources. The innovative Homomorphic Binary Decision Tree (HBDT) method utilizes a modified Gini Impurity index (MGI) for node splitting in encrypted data scenarios. Notably, the proposed training approach operates in …

binary cloud cloud environments data data privacy decision encryption environments eprint report forest forests homomorphic encryption kim lee privacy random report training trees