用差分隐私提升深度学习模型泛化能力，防止过拟合。
Preventing overfitting in deep learning using differential privacy
- 通过差分隐私机制约束训练过程，限制模型对训练数据的过度记忆。
- 在有限数据下显著改善模型在未见数据上的表现，降低过拟合风险。
- 适合关注隐私保护与模型鲁棒性的研究人员和工程师。
深度神经网络在现实世界中的应用日益广泛，在图像、语音和文本等众多数据集上取得了领先性能。它们能够从数据中学习复杂的关系和抽象特征，但这也使模型容易学习训练集中的噪声，导致过拟合或泛化能力下降。在实际场景中，分析师通常面临数据量有限的问题，需要构建能推广到未知数据的模型。本文探索了基于差分隐私的方法，以提升深度神经网络的泛化能力，通过在训练过程中引入隐私保护机制，减少模型对特定训练样本的依赖，从而增强其在新数据上的表现。
原文摘要 · Abstract (English)
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.
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