Practical Anonymous Two-Party Gradient Boosting Decision Tree 事件

Practical Anonymous Two-Party Gradient Boosting Decision Tree arXiv:2605.26903v1 Announce Type: cross Abstract: Structured data is well handled by gradient-boosted decision trees (GBDT), which are usually trained on vertically partitioned features across mutually distrustful parties. High speed and interpretability make GBDTs popular in finance and healthcare, where neural networks may fall short. Enabling secure computation for GBDTs poses unique challenges, requiring secure record alignment f