Machine learning (ML) models can memorize training datasets. As a result,
training ML models over private datasets can violate the privacy of
individuals. Differential privacy (DP) is a rigorous privacy notion to preserve
the privacy of underlying training datasets in ML models. Yet, training ML
models in a DP framework usually degrades the accuracy of ML models. This paper
aims to boost the accuracy of a DP-ML model, specifically a logistic regression
model, via a pre-training module. In more detail, we initially pre-train our
model on a public training dataset that there is no privacy concern about it.
Then, we fine-tune our model via the DP logistic regression with the private
dataset. In the numerical results, we show that adding a pre-training module
significantly improves the accuracy of the DP logistic regression.

此研究旨在通过一个预训练模块提高 DP-ML 模型，特别是逻辑回归模型的准确性，通过在没有隐私问题的公共训练数据集上对模型进行首次训练，然后通过 DP 逻辑回归和私有数据集对模型进行微调，数值结果显示，增加预训练模块显著提高了 DP 逻辑回归的准确性。