DynFrs: An Efficient Framework for Machine Unlearning in Random Forest

TL;DR

DynFrs is a novel framework that enables fast and accurate machine unlearning in Random Forests, addressing privacy regulations like GDPR with minimal impact on model performance.

Contribution

It introduces the DynFrs framework utilizing subsampling and lazy update strategies to efficiently perform machine unlearning in Random Forests, adaptable to various variants.

Findings

Achieves significantly faster unlearning performance.

Maintains or improves predictive accuracy.

Applicable to different Random Forest variants.

Abstract

Random Forests are widely recognized for establishing efficacy in classification and regression tasks, standing out in various domains such as medical diagnosis, finance, and personalized recommendations. These domains, however, are inherently sensitive to privacy concerns, as personal and confidential data are involved. With increasing demand for the right to be forgotten, particularly under regulations such as GDPR and CCPA, the ability to perform machine unlearning has become crucial for Random Forests. However, insufficient attention was paid to this topic, and existing approaches face difficulties in being applied to real-world scenarios. Addressing this gap, we propose the DynFrs framework designed to enable efficient machine unlearning in Random Forests while preserving predictive accuracy. Dynfrs leverages subsampling method Occ(q) and a lazy tag strategy Lzy, and is still adaptable to any Random Forest variant. In essence, Occ(q) ensures that each sample in the training set occurs only in a proportion of trees so that the impact of deleting samples is limited, and Lzy delays the reconstruction of a tree node until necessary, thereby avoiding unnecessary modifications on tree structures. In experiments, applying Dynfrs on Extremely Randomized Trees yields substantial improvements, achieving orders of magnitude faster unlearning performance and better predictive accuracy than existing machine unlearning methods for Random Forests.