Welcome to the resource topic for 2024/529
Title:
Fully Homomorphic Training and Inference on Binary Decision Tree and Random Forest
Authors: Hojune Shin, Jina Choi, Dain Lee, Kyoungok Kim, Younho Lee
Abstract: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 a single cloud security domain without the need for decryption, addressing key challenges in privacy-preserving machine learning.
We also propose an efficient method for inference utilizing only addition for path evaluation even when both models and inputs are encrypted, achieving O(1) multiplicative depth.
Experiments demonstrate that this method surpasses the previous study by Akavia et al.'s by at least 3.7 times in the speed of inference. The study also expands to privacy-preserving random forests, with GPU acceleration ensuring feasibly efficient performance in both training and inference.
ePrint: https://eprint.iacr.org/2024/529
See all topics related to this paper.
Feel free to post resources that are related to this paper below.
Example resources include: implementations, explanation materials, talks, slides, links to previous discussions on other websites.
For more information, see the rules for Resource Topics .