A differentially private greedy decision forest classification algorithm with high utility

Abstract

The rapid development of data analysis technologies and the easily accessible datasets have enabled the construction of a comprehensive analytics model, which can facilitate the decision makings involved in services. Meanwhile, the individual privacy preservation is of great necessity. Decision tree is a common method in medical prediction and diagnose, known for its simplicity of understanding and interpreting. However, the process of building a decision tree might cause individual privacy disclosure. Differential privacy provides a rigorous mathematical definition of privacy by controlling the risk of privacy leakage in a manageable range while maintaining the statistical characteristics. In this paper, we propose a Differentially Private Greedy Decision Forest with high utility (DPGDF) to build a privacy-preserving decision forest. In DPGDF, we design a novel budget allocation strategy that allows the nodes in greater depth get more privacy budgets in the decision tree construction process, which can, to some extent, mitigate the problem of excessive noises introduced to the leaf nodes. To aggregate multiple trees into a forest, we propose a selective aggregation method based on the prediction accuracy of the decision forest. In addition, we develop an iterative method to speed up the process of selective aggregation. Finally, we experimentally prove that the proposed DPGDF can achieve a better performance on two practical datasets compared with other algorithms.