Lumberjack: Heavy Hitter Detection Improves Differentially Private Random Forests
arXiv·medium signal
Proposes a novel approach to privacy-preserving random forests by applying heavy hitter detection in tree construction, significantly improving accuracy under differential privacy guarantees. Random forests are the workhorse of regulated industries (healthcare, finance) where tabular data dominates and privacy regulations are strict. Method works with existing DP frameworks.