Explore the challenges and solutions of implementing differentially private machine learning in healthcare settings through this 19-minute conference talk from FAccT 2021. Delve into the unique aspects of healthcare data, examine the extreme tradeoffs involved, and understand the concept of group fairness defined by influence. Learn about differentially private training techniques, important considerations for implementation, and future directions in this field. Gain insights into how privacy-preserving methods can be applied to sensitive health data while maintaining utility and fairness in predictive models.