2 papers
cs.LG2019
DP-MAC: The Differentially Private Method of Auxiliary Coordinates for Deep Learning
Frederik Harder, Jonas Köhler, Max Welling +1
Developing a differentially private deep learning algorithm is challenging, due to the difficulty in analyzing the sensitivity of objective functions that are typically used to tra…
cs.LG2019
Interpretable and Differentially Private Predictions
Frederik Harder, Matthias Bauer, Mijung Park
Interpretable predictions, where it is clear why a machine learning model has made a particular decision, can compromise privacy by revealing the characteristics of individual data…