Mitigating Disparate Impact of Differentially Private Learning through Bounded Adaptive Clipping
This addresses fairness issues in privacy-preserving ML for minority groups, representing an incremental improvement to existing DP methods.
The paper tackles the problem of differential privacy methods having disparate impacts on minority groups in machine learning models, showing that bounded adaptive clipping improves worst-class accuracy by over 10 percentage points compared to unbounded adaptive clipping and over 5 percentage points compared to constant clipping on skewed datasets.
Differential privacy (DP) has become an essential framework for privacy-preserving machine learning. Existing DP learning methods, however, often have disparate impacts on model predictions, e.g., for minority groups. Gradient clipping, which is often used in DP learning, can suppress larger gradients from challenging samples. We show that this problem is amplified by adaptive clipping, which will often shrink the clipping bound to tiny values to match a well-fitting majority, while significantly reducing the accuracy for others. We propose bounded adaptive clipping, which introduces a tunable lower bound to prevent excessive gradient suppression. Our method improves the accuracy of the worst-performing class on average over 10 percentage points on skewed MNIST and Fashion MNIST compared to the unbounded adaptive clipping, and over 5 percentage points over constant clipping.