Differential Privacy Mechanisms in Neural Tangent Kernel Regression

Training data privacy is a fundamental problem in modern Artificial Intelligence (AI) applications such as face recognition recommendation systems language generation and many others as it may contain sensitive user information related to legal issues. To fundamentally understand how privacy mechanisms work in AI applications we study differential privacy (DP) in the Neural Tangent Kernel (NTK) regression setting where DP is one of the most powerful tools for measuring privacy under statistical learning and NTK is one of the most popular analysis frameworks for studying the learning mechanisms of deep neural networks. In our work we can show provable guarantees for both differential privacy and test accuracy of our NTK regression. Furthermore we conduct experiments on the basic image classification dataset CIFAR10 to demonstrate that NTK regression can preserve good accuracy under a modest privacy budget supporting the validity of our analysis. To our knowledge this is the first work to provide a DP guarantee for NTK regression.