Training machine learning models based on neural networks requires large datasets, which may contain sensitive information. The models, however, should not expose private information from these datasets. Differentially private SGD [DP-SGD] requires the modification of the standard stochastic gradient descent [SGD] algorithm for training new models. In this short paper, a novel regularization strategy is proposed to achieve the same goal in a more efficient manner.

翻译：基于神经网络的机器学习模型训练需要大规模数据集，这些数据集可能包含敏感信息。然而，模型不应泄露这些数据集中的隐私信息。差分隐私随机梯度下降[DP-SGD]需要修改标准的随机梯度下降[SGD]算法以训练新模型。在这篇短文中，我们提出了一种新颖的正则化策略，能以更高效的方式实现相同目标。