Existing work on trustworthy machine learning (ML) often concentrates on individual aspects
of trust, such as fairness or privacy. Additionally, many techniques overlook the distinction …

Machine learning (ML) models cannot neglect risks to security, privacy, and fairness.
Several defenses have been proposed to mitigate such risks. When a defense is effective in …

In this work, we investigate binary classification under the constraints of both differential
privacy and fairness. We first propose an algorithm based on the decoupling technique for …