Preventing overfitting in deep learning using differential privacy

TL;DR

This paper investigates how differential privacy techniques can be employed to prevent overfitting in deep neural networks, thereby enhancing their generalization capabilities in practical scenarios.

Contribution

It introduces a differential privacy-based method specifically designed to improve the generalization of deep neural networks and mitigate overfitting issues.

Findings

Differential privacy can effectively reduce overfitting in deep learning models.

The proposed approach enhances model generalization on unseen data.

Empirical results demonstrate improved performance with privacy-preserving training.

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.