不完全数据集的差分隐私保护决策树研究 (Method of Constructing Differential Privacy Decision Tree Classifier with Incomplete Data Sets)

Siqian Shen, Yuguang Mao, Guanru Jiang

Abstract: We mainly studied the problem of constructing differential privacy decision tree classifier with incomplete data sets. We first introduced the differential privacy ID3 decision tree algorithm and differentially private random decision tree algorithm. Then we considered the weakness of the algorithms talked above, and created a new differentially private random decision tree algorithm with exponential mechanism. Finally, an approach for decision tree classifier with incomplete data sets was proposed, which yields better prediction while maintaining good privacy without inserting values, called WP (Weight Partition). And the experimental results show that our approach is suitable for either differential privacy ID3 decision trees or differentially private random decision trees, either laplace or exponential mechanism.