Improving Private Random Forest Prediction Using Matrix Representation

Abstract

We introduce a novel matrix representation for differentially private training and prediction methods tailored to random forest classifiers. Our approach involves representing each root-to-leaf decision path in all trees as a row vector in a matrix. Similarly, inference queries are represented as a matrix. This representation enables us to collectively analyze privacy across multiple trees and inference queries, resulting in optimal DP noise allocation under the Laplace Mechanism. Our experimental results show significant accuracy improvements of up to 40% compared to state-of-the-art methods.