Lumberjack introduces a novel (ε,δ)-DP heavy hitter detection algorithm for hierarchical data with O(√log h) error that allows deeper trees and yields a new state-of-the-art privacy-utility tradeoff for random forests.
[2020] sort values of continuous attributes and use the average of buckets with 5 data points as candidates for the splitpoint (Algorithm 1, Line 10)
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Lumberjack: Better Differentially Private Random Forests through Heavy Hitter Detection in Trees
Lumberjack introduces a novel (ε,δ)-DP heavy hitter detection algorithm for hierarchical data with O(√log h) error that allows deeper trees and yields a new state-of-the-art privacy-utility tradeoff for random forests.