Lumberjack: Better Differentially Private Random Forests through Heavy Hitter Detection in Trees
arXiv:2605.22756v1 Announce Type: new Abstract: Random forests are widely used in fields involving sensitive tabular data, but existing approaches to enforcing differential privacy (DP) typically degrade performance to the point of impracticality. In this paper, we introduce Lumberjack, a differentially private random forest algorithm that achieves substantially higher utility by constructing large random decision trees and then applying aggressive, privacy-preserving pruning to retain only sufficiently populated nodes. A key component of our approach is a novel $(\varepsilon,\delta)$-DP heavy
The proliferation of sensitive data and increasing regulatory scrutiny around data privacy is accelerating the search for robust differential privacy solutions in machine learning.
Improved differentially private random forests can enable the wider, safer use of AI in fields handling sensitive data (e.g., healthcare, finance), balancing utility with privacy compliance.
The trade-off between model performance and differential privacy in random forests becomes less severe, potentially allowing for practical application in real-world scenarios without rendering models impractical.
- · Healthcare sector
- · Financial services
- · AI/ML researchers
- · Data privacy solution providers
- · Organizations with poor data governance
- · Less efficient differential privacy techniques
More widespread adoption of differentially private machine learning models in privacy-sensitive industries.
Increased trust in AI applications that process personal or confidential information, potentially boosting public acceptance.
New regulatory standards or best practices emerging that mandate the use of more effective DP techniques due to their enhanced practicality.
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