Accuracy Improvement in Differentially Private Logistic Regression: A Pre-training Approach

Machine learning (ML) models can memorize training datasets. As a result, training ML models over private datasets can lead to the violation of individuals' privacy. Differential privacy (DP) is a rigorous privacy notion to preserve the privacy of underlying training datasets. 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 logistic regression (LR) via a pre-training module. In more detail, we initially pre-train our …

accuracy can cs.cr cs.lg datasets differential privacy framework improvement machine machine learning ml models notion privacy private result training