Preventing overfitting in deep learning using differential privacy

arXiv cs.LG / 4/21/2026

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Key Points

Deep neural networks can achieve state-of-the-art results but are vulnerable to overfitting, where they learn noise in the training data and generalize poorly.
Analysts in real-world deployments often face limited data, making reliable generalization to unseen inputs especially challenging.
The paper investigates a differential-privacy–based approach as a method to improve generalization in deep neural networks.
The work positions differential privacy as a practical strategy to reduce the negative effects of overfitting by constraining how models learn from data.

Abstract

The use of Deep Neural Network based systems in the real world is growing. They have achieved state-of-the-art performance on many image, speech and text datasets. They have been shown to be powerful systems that are capable of learning detailed relationships and abstractions from the data. This is a double-edged sword which makes such systems vulnerable to learning the noise in the training set, thereby negatively impacting performance. This is also known as the problem of \emph{overfitting} or \emph{poor generalization}. In a practical setting, analysts typically have limited data to build models that must generalize to unseen data. In this work, we explore the use of a differential-privacy based approach to improve generalization in Deep Neural Networks.