Motivated by settings in which predictive models may be required to be non-discriminatory
with respect to certain attributes (such as race), but even collecting the sensitive attribute …

Sensitive attributes such as race are rarely available to learners in real world settings as
their collection is often restricted by laws and regulations. We give a scheme that allows …

As machine learning algorithms are deployed on sensitive data in critical decision making
processes, it is becoming increasingly important that they are also private and fair. In this …

We theoretically study the impact of differential privacy on fairness in classification. We prove
that, given a class of models, popular group fairness measures are pointwise Lipschitz …

The objective of machine learning is to extract useful information from data, while privacy is
preserved by concealing information. Thus it seems hard to reconcile these competing …

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

In this work, we investigate whether privacy and fairness can be simultaneously achieved by
a single classifier in several different models. Some of the earliest work on fairness in …

Abstract Machine learning is increasingly becoming a powerful tool to make decisions in a
wide variety of applications, such as medical diagnosis and autonomous driving. Privacy …

Differential Privacy (DP) is an important privacy-enhancing technology for private machine
learning systems. It allows to measure and bound the risk associated with an individual …

Privacy-preserving machine learning algorithms are crucial for the increasingly common
setting in which personal data, such as medical or financial records, are analyzed. We …