Accuracy Amplification in Differentially Private Logistic Regression: A Pre-Training Approach
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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.
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