Differentially private ensemble learning for classification

Training machine learning models requires large amounts of data, which may contain personal sensitive information. Machine learning based on privacy protection has become a research hotspot. In this paper, differential privacy is applied to the ensemble learning, a branch of machine learning, to prevent privacy leakage in the classification process. We propose a differentially private ensemble learning algorithm for classification, which achieves privacy protection while ensures prediction accuracy. Firstly, we adopt the Bag of Little Bootstrap technique and the Jaccard similarity coefficient to generate a set of training data sets, and construct corresponding differentially private base classifiers by adding a carefully chosen amount of perturbation noise with a privacy budget allocation strategy. Furthermore, to reduce the impact of perturbation noise on the accuracy of prediction, an effective ensemble algorithm is proposed. Specifically, the base classifiers are selected based on some criterion functions, and the corresponding weights are assigned simultaneously. Then, the final result of the classification is obtained by a weighted voting scheme. Experiments are executed on 9 real data sets from the UCI Machine Learning Repository to demonstrate that our differentially private ensemble classification algorithm achieves a better trade-off in terms of privacy protection and prediction accuracy. (c) 2020 Elsevier B.V. All rights reserved.

Automated summary

This study applies differential privacy techniques to ensemble learning, proposing an algorithm that achieves a balance between privacy protection and prediction accuracy in classification.