Differential Privacy in Machine Learning

differential privacy is a state-of-the-art technique to guarantee that if an adversary has access to your model or outputs from your model, they can't recover underlying information. In healthcare data, outliers are minority patients. When you apply differential privacy in a vanilla way to learning in healthcare data, you noise and clip in our studies black patients. And so you're losing predictive performance and influence of minority patients by default.