Machine learning has the potential to improve health care through its ability to extract information from data. Unfortunately, machine learning is susceptible to privacy attacks which leak information about the data it was trained on. This can have dire consequences in health care where protecting patient privacy is of the utmost importance. Differential privacy has been proposed as the leading technique to defend against privacy attacks and has had successful use by the US Census, Google, and Apple. This talk will present the challenges of using differentially private machine learning in health care and how future solutions might address them.