Differentially Private Greedy Decision Forest

Abstract

As information security is increasingly valued, privacy-preserving data mining has become a research hotspot in the field of big data and signal processing. We propose a new differentially private greedy decision forest algorithm called DPGDF to help improve the accuracy of privacy-preserving data mining. Unlike previous algorithms that only employed greedy decision trees or random forests, our algorithm uses a combination of greedy trees and parallel combination theory to construct a greedy decision forest and coordinate privacy protection and prediction accuracy to achieve the best balance. Combined with smooth sensitivity, the introduction of noise is minimized, making the prediction accuracy of the algorithm notably better than the current state-of-the-art algorithms. Experiments on the UCI datasets show that the prediction accuracy of our algorithm is about 10% higher than that of those algorithms.