Optimal Differentially Private Model Training with Auxiliary Public Data

Leveraging public data can improve the accuracy of differentially private machine learning models, but there are fundamental limits on the benefits that can be achieved in the worst case. Novel semi-differentially private algorithms can outperform the naive approaches by better utilizing the public and private data.