InPrivate Digging: Enabling Tree-based Distributed Data Mining with Differential Privacy
Abstract
Data mining has heralded the major breakthrough in data analysis, serving as a “super cruncher” to discover hidden information and valuable knowledge in big data systems. For many applications, the collection of big data usually involves various parties who are interested in pooling their private data sets together to jointly train machine-learning models that yield more accurate prediction results. However, data owners may not be willing to disclose their own data due to privacy concerns, making it imperative to provide privacy guarantee in collaborative data mining over distributed data sets. In this paper, we focus on tree-based data mining. To begin with, we design novel privacy-preserving schemes for two most common tasks: regression and binary classification, where individual data owners can perform training locally in a differentially private manner. Then, for the first time, we design and implement a privacy-preserving system for gradient boosting decision tree (GBDT), where different regression trees trained by multiple data owners can be securely aggregated into an ensemble. We conduct extensive experiments to evaluate the performance of our system on multiple real-world data sets. The results demonstrate that our system can provide a strong privacy protection for individual data owners while maintaining the prediction accuracy of the original trained model.
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Hitaj et al. apply GAN attack which allows the adversarial party in the training process of FL to fabricate inferring class representative.
“Hitaj et al. [155] apply GAN attack which allows the adversarial party in the training process of FL to fabricate inferring class representative.”
From Challenges, Applications and Design Aspects of Federated Learning: A Survey (2021) · cites this paper as [155]
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Chatel, S., Pyrgelis, A., Troncoso-Pastoriza, J. R., & Hubaux, J. (2021) · Proceedings on Privacy Enhancing Technologies · doi:10.2478/popets-2021-0043 · Cited by 17 · see citing works
An Efficient Speculative Federated Tree Learning System With a Lightweight NN-Based Predictor
Zhang, Y., Liao, H., Zhao, L., Shao, Y., Tian, Z., Wang, X., Meng, D., & Hou, R. (2025) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2025.3581295 · Cited by 4 · see citing works
Self-citationDecision Tree-based Privacy Protection in Federated Learning: A Survey
Wang, Z., Guo, H., & Gai, K. (2024) · IEEE Conference on Big Data Security on Cloud (BigDataSecurity) · doi:10.1109/bigdatasecurity62737.2024.00028 · Cited by 4 · see citing works
Jafarigol, E., Trafali̇s, T. B., Razzaghi, T., & Zamankhani, M. (2024) · Springer optimization and its applications · doi:10.1007/978-3-031-74006-0_4 · Cited by 4 · see citing works
A Differentially Private Random Decision Tree Classifier with High Utility
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Gradient Boosting Forest: a Two-Stage Ensemble Method Enabling Federated Learning of GBDTs
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Practical differentially private online advertising
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Self-citationDifferentially Private ERM Based on Data Perturbation.Preprint
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A Novel Privacy-Preserved Recommender System Framework Based On Federated Learning
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Survey of privacy preserving federated tree-based learning
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A differentially private distributed collaborative XGBoost method
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- Authors
- Zhao, L., Ni, L., Hu, S., Chen, Y., Zhou, P., Xiao, F., & Wu, L.
- Affiliations
- Wuhan University (CN)
- City University of Hong Kong (HK)
- Huazhong University of Science and Technology (CN)
- Nanjing University of Posts and Telecommunications (CN)
- Venue
- IEEE INFOCOM 2018 - IEEE Conference on Computer Communications
- Publisher
- IEEE
- Published
- 2018-04
- Pages
- 2087-2095
- OpenAlex
- W2793685318
- Cited by
- 108
- References
- 31
- Citations by year
- 2026: 8 · 2025: 6 · 2024: 23 · 2023: 13 · 2022: 18 · 2021: 23 · 2020: 10 · 2019: 6 · 2018: 1
- FWCI
- 4.08
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Conference on Computer Communications (INFOCOM) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Cryptography and Data Security
- Physical Sciences › Computer Science › Information Systems › Cloud Data Security Solutions
- Keywords
- differential privacy, distributed data mining, gradient boosting decision tree, binary classification, ensemble learning
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