Practical Anonymous Two-Party Gradient Boosting Decision Tree
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 for comparison. Relying on private set intersection (PSI) is a de facto approach. Mistaking PSI for a safety measu
Record details
Published: 26 May 2026
Source: arXiv
Category: Research
Topics: Safety & alignment · Healthcare
Retrieved: 14 July 2026
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ethics.ai (26 May 2026), “Practical Anonymous Two-Party Gradient Boosting Decision Tree,” evidence record 3682, https://ethics.ai/record/3682 (originally published by arXiv).
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