DeltaBoost: Gradient Boosting Decision Trees with Efficient Machine Unlearning

As machine learning (ML) has been widely developed in real-world applications, the privacy of ML models draws an increasing concern. In this paper, we study how to forget specific data records from ML models to preserve the privacy of these data. Although some studies propose efficient unlearning algorithms on random forests and extremely randomized trees, Gradient Boosting Decision Trees (GBDT), which are widely used in practice, have not been explored. The efficient unlearning of GBDT faces two major challenges: 1) the training of each tree is deterministic and non-robust; 2) the training of a tree depends on all the previous trees. To solve the first challenge, we propose a robust GBDT-like ML model DeltaBoost that enables efficient and accurate deletion according to our theoretical analysis. For the second challenge, we design a training algorithm for DeltaBoost that minimizes the dependency among trees. Our experiments on five datasets demonstrate that DeltaBoost can remove data records from the trained model efficiently and effectively. Our unlearning approach achieves up to two orders of magnitude speedup compared to retraining GBDT. Besides, DeltaBoost produces competitive performance to existing decision-tree-based ML models.