AAAI 2025technical0 citations
Improving Private Random Forest Prediction Using Matrix Representation
Arisa Tajima, Joie Wu, Amir Houmansadr
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.
BibTeX
@article{Tajima_Wu_Houmansadr_2025, title={Improving Private Random Forest Prediction Using Matrix Representation}, volume={39}, url={https://ojs.aaai.org/index.php/AAAI/article/view/34289}, DOI={10.1609/aaai.v39i19.34289}, abstractNote={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.}, number={19}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Tajima, Arisa and Wu, Joie and Houmansadr, Amir}, year={2025}, month={Apr.}, pages={20770-20777} }