Research map / Federated learning and privacy / Differential privacy
Privacy-Preserving Gradient Boosting Decision Trees
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use DPBoost (differentially private GBDT) instead of non-private training | LightGBM · training time per tree across 10 datasets | training time per tree (s): 0.019 -> 0.007 on adult (NP vs DPBoost); 2.97 vs 0.82 on real-sim | Hurt |
| use DPBoost instead of PARA and SEQ at small privacy budget | DP GBDT · classification datasets, epsilon=1 | test error: 10% lower test errors on average | Helped |
Closest papers by meaning
- Practical Federated Gradient Boosting Decision Trees (AAAI 2020 · similarity 0.88)
- An Improved Privacy and Utility Analysis of Differentially Private SGD with Bounded Domain and Smooth Losses (AAAI 2026 · similarity 0.88)
- Quantized Training of Gradient Boosting Decision Trees (NeurIPS 2022 · similarity 0.87)
- Certification for Differentially Private Prediction in Gradient-Based Training (ICML 2025 · similarity 0.87)
- Online Sensitivity Optimization in Differentially Private Learning (AAAI 2024 · similarity 0.86)
- Differentially Private SGD Without Clipping Bias: An Error-Feedback Approach (ICLR 2024 · similarity 0.85)
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