Privacy in Machine Learning

An important aspect of responsible AI usage is ensuring that ML models are prevented from exposing potentially sensitive information, such as demographic information or other attributes in the training dataset that could be used to identify people. One way to achieve this is by using differentially private stochastic gradient descent (DP-SGD), which is a modification to the standard stochastic gradient descent (SGD) algorithm in machine learning.

Models trained with DP-SGD have measurable differential privacy (DP) improvements, which helps mitigate the risk of exposing sensitive training data. Since the purpose of DP is to help prevent individual data points from being identified, a model trained with DP should not be affected by any single training example in its training data set. DP-SGD techniques can also be used in federated learning to provide user-level differential privacy. You can learn more about differentially private deep learning in the original paper.