Decision Tree Classification with Differential Privacy: A Survey

Abstract

Data mining information about people is becoming increasingly important in the data-driven society of the 21st century. Unfortunately, sometimes there are real-world considerations that conflict with the goals of data mining; sometimes the privacy of the people being data mined needs to be considered. This necessitates that the output of data mining algorithms be modified to preserve privacy while simultaneously not ruining the predictive power of the outputted model. Differential privacy is a strong, enforceable definition of privacy that can be used in data mining algorithms, guaranteeing that nothing will be learned about the people in the data that could not already be discovered without their participation. In this survey, we focus on one particular data mining algorithm -- decision trees -- and how differential privacy interacts with each of the components that constitute decision tree algorithms. We analyze both greedy and random decision trees, and the conflicts that arise when trying to balance privacy requirements with the accuracy of the model.

Pre-print of paper accepted in ACM Computing Surveys, 35 pages

References in corpus (4)

• Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model
• Differential Privacy and Machine Learning: a Survey and Review
• Differentially Private Random Decision Forests using Smooth Sensitivity
• Differentially- and non-differentially-private random decision trees

Cited by in corpus (2)

• How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy
• On Tackling Explanation Redundancy in Decision Trees