mlsanityData to 7 Oct 2026

SoK: Privacy-Preserving Collaborative Tree-based Model Learning

arXiv 2103.08987 ↗First posted 16 Mar 2021cs.CR cs.LGcited by 4

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Abstract (arXiv)

Tree-based models are among the most efficient machine learning techniques for data mining nowadays due to their accuracy, interpretability, and simplicity. The recent orthogonal needs for more data and privacy protection call for collaborative privacy-preserving solutions. In this work, we survey the literature on distributed and privacy-preserving training of tree-based models and we systematize its knowledge based on four axes: the learning algorithm, the collaborative model, the protection mechanism, and the threat model. We use this to identify the strengths and limitations of these works and provide for the first time a framework analyzing the information leakage occurring in distributed tree-based model learning.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 39 of its 194 references that are in the map. First posted on arXiv 16 Mar 2021.

Builds on

39 of 194 references.

References · 39

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  12. 2020When Machine Unlearning Jeopardizes Privacycited by 181
27 more
  1. 2019SecureBoost: A Lossless Federated Learning Frameworkcited by 174
  2. 2013Hacking Smart Machines with Smarter Ones: How to Extract Meaningful Data from Machine Learning Classifierscited by 128
  3. 2019Updates-Leak: Data Set Inference and Reconstruction Attacks in Online Learningcited by 117
  4. 2020Privacy Preserving Vertical Federated Learning for Tree-based Modelscited by 89
  5. 2019Practical Federated Gradient Boosting Decision Treescited by 53
  6. 2019Federated Forestcited by 49
  7. 2019Provably Robust Boosted Decision Stumps and Trees against Adversarial Attackscited by 26
  8. 2019Privacy-Preserving Gradient Boosting Decision Treescited by 20
  9. 2019SecureGBM: Secure Multi-Party Gradient Boostingcited by 19
  10. 2016Decision Tree Classification with Differential Privacy: A Surveycited by 17
  11. 2016Differentially Private Random Decision Forests using Smooth Sensitivitycited by 16
  12. 2017Leaky Cauldron on the Dark Land: Understanding Memory Side-Channel Hazards in SGXcited by 15
  13. 2019The Tradeoff Between Privacy and Accuracy in Anomaly Detection Using Federated XGBoostcited by 13
  14. 2019Boosting Privately: Privacy-Preserving Federated Extreme Boosting for Mobile Crowdsensingcited by 11
  15. 2020Secure Collaborative Training and Inference for XGBoostcited by 9
  16. 2019Revocable Federated Learning: A Benchmark of Federated Forestcited by 6
  17. 2020Scalable and Provably Accurate Algorithms for Differentially Private Distributed Decision Tree Learningcited by 5
  18. 2014Differentially- and non-differentially-private random decision treescited by 4
  19. 2017What Does The Crowd Say About You? Evaluating Aggregation-based Location Privacycited by 4
  20. 2015Encrypted statistical machine learning: new privacy preserving methodscited by 3
  21. 2018Privacy-Preserving Collaborative Prediction using Random Forestscited by 3
  22. 2016Predicting the future relevance of research institutions - The winning solution of the KDD Cup 2016cited by 2
  23. 2020Cloud-based Federated Boosting for Mobile Crowdsensingcited by 2
  24. 2020Boosted and Differentially Private Ensembles of Decision Treescited by 1
  25. 2020Privacy-Preserving Boosting in the Local Settingcited by 1
  26. 2020Federated Extra-Trees with Privacy Preservingcited by 1
  27. 2020Balance is key: Private median splits yield high-utility random treescited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.

  1. 2025Tree-based Models for Vertical Federated Learning: A Surveycited by 3
  2. 2025TimberStrike: Dataset Reconstruction Attack Revealing Privacy Leakage in Federated Tree-Based Systemscited by 0
  3. 2026SoK: Enhancing Cryptographic Collaborative Learning with Differential Privacycited by 0
  4. 2026Practical Anonymous Two-Party Gradient Boosting Decision Treecited by 0