Bounding User Contributions: A Bias-Variance Trade-off in Differential Privacy

Kareem Amin, Alex Kulesza, Andres Muñoz Medina, Sergei Vassilvitskii

Abstract: Differentially private learning algorithms protect individual participants in the training dataset by guaranteeing that their presence does not significantly change the resulting model. In order to...