Differential Privacy Has Disparate Impact on Model Accuracy

Differential privacy (DP) is a popular mechanism for training machine learning models with bounded leakage about the presence of specific points in the training data. The cost of differential privacy is a reduction in the model's accuracy. We demonstrate that this cost is not borne equally: accuracy of DP models drops much more for the ...More