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S-BDT: Distributed Differentially Private Boosted Decision Trees

arXiv 2309.12041 ↗First posted 21 Sep 2023cs.CR cs.LGcited by 0

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Abstract (arXiv)

We introduce S-BDT: a novel (varepsilon,δ)-differentially private distributed gradient boosted decision tree (GBDT) learner that improves the protection of single training data points (privacy) while achieving meaningful learning goals, such as accuracy or regression error (utility). S-BDT uses less noise by relying on non-spherical multivariate Gaussian noise, for which we show tight subsampling bounds for privacy amplification and incorporate that into a R'enyi filter for individual privacy accounting. We experimentally reach the same utility while saving 50% in terms of epsilon for varepsilon ≤ 0.5 on the Abalone regression dataset (dataset size ≈ 4K), saving 30% in terms of epsilon for varepsilon ≤ 0.08 for the Adult classification dataset (dataset size ≈ 50K), and saving 30% in terms of epsilon for varepsilon≤0.03 for the Spambase classification dataset (dataset size ≈ 5K). Moreover, we show that for situations where a GBDT is learning a stream of data that originates from different subpopulations (non-IID), S-BDT improves the saving of epsilon even further.

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References · 14

  1. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
  2. 2016Deep Learning with Differential Privacycited by 2,728
  3. 2017Renyi Differential Privacycited by 650
  4. 2018Privacy Amplification by Subsampling: Tight Analyses via Couplings and Divergencescited by 242
  5. 2020The Discrete Gaussian for Differential Privacycited by 135
  6. 2019Hypothesis Testing Interpretations and Renyi Differential Privacycited by 85
  7. 2020Individual Privacy Accounting via a Renyi Filtercited by 59
  8. 2021Accuracy, Interpretability, and Differential Privacy via Explainable Boostingcited by 24
  9. 2019Privacy-Preserving Gradient Boosting Decision Treescited by 20
  10. 2022Individual Privacy Accounting with Gaussian Differential Privacycited by 20
  11. 2016Differentially Private Random Decision Forests using Smooth Sensitivitycited by 16
  12. 2022Federated Boosted Decision Trees with Differential Privacycited by 9
2 more
  1. 2018MVG Mechanism: Differential Privacy under Matrix-Valued Querycited by 7
  2. 2014Differentially- and non-differentially-private random decision treescited by 4

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