DIFFERENTIALLY PRIVATE REGRESSION TREE AND FOREST
Main Article Content
Abstract
Data modeling is an important problem in data analysis as well as machine learning. There exist many different data modeling solutions, of which regression tree is a method which has many advantages compared to other regression methods. In addition to the accuracy and interpretability of the result model, the issue of ensuring the privacy of the training dataset is also very important and urgent, especially with sensitive and personal data. This paper proposes basic methods and algorithms to build privacy-preserving regression trees based on the differential privacy techniques and algorithms. The experimental results indicate the feasibility of the proposed methods, while also raise challenges which could be further studied.
Keywords
differential privacy, privacy-preserving data analysis, regression, regression tree
Article Details
References
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