Title: Effectively using  public data in privacy preserving Machine learning. Abstract: A key challenge towards differentially private machine learning is balancing the trade-off between privacy and utility. 
A recent line of work has demonstrated that leveraging  \emph{public data samples} can enhance the utility of DP-trained models (for the same privacy guarantees). 
In this work, we show that public data can be used to improve utility in DP models significantly more than shown in recent works.  
Towards this end, we introduce a modified DP-SGD algorithm that leverages public data during its training process. 
Our technique uses public data in two complementary ways: (1) it uses generative models trained on public data to produce synthetic data that is effectively embedded in multiple steps of the training pipeline; (2) it uses a new gradient clipping mechanism  (required for achieving differential privacy) which changes the \emph{origin} of gradient vectors using information inferred from available public and synthesized data. 
Our experimental results demonstrate the effectiveness of our approach in improving the state-of-the-art in differentially private machine learning across multiple datasets, network architectures, and application domains. 
Notably, we achieve a $75\%$ accuracy on CIFAR10  when using only $2,000$ public images;  this is \emph{significantly higher} than the  state-of-the-art which is $68\%$  for DP-SGD with the privacy budget of $\varepsilon=2,\delta=10^{-5}$ (given the same number of public data points).