An Efficient Differential Privacy Logistic Classification Mechanism

The logistic model is a very elementary and important model in the field of machine learning. In this article, an efficient differential privacy logistic classification mechanism is proposed. The proposed mechanism is better than object function perturbation mechanism in terms of running time and accuracy. Regarding accuracy, the proposed mechanism's accuracy is almost the same as the no differential privacy (non-dp) mechanism, and the proposed mechanism is better than that of the object function perturbation mechanism in both the test accuracy and the train accuracy. As for the running time of the training model, the proposed mechanism is better than the object function mechanism and is the same as the non-dp mechanism.

I. Introduction

Logistic model deserves to be studied more. It is one of the most elementary and important models of machine learning. Now, it is used in many application scenarios that are meaningful in our daily life, such as medical industry, financial business, and transport industry. For example, it can be used to help administrators to make better decisions about bus dispatch.