Practical and pervasive needs for robustness and privacy in algorithms have inspired the
design of online adversarial and differentially private learning algorithms. The primary …

This paper surveys recent work in the intersection of differential privacy (DP) and fairness. It
reviews the conditions under which privacy and fairness may have aligned or contrasting …

As deep learning models mature, one of the most prescient questions we face is: what is the
ideal tradeoff between accuracy, fairness, and privacy (AFP)? Unfortunately, both the privacy …

We develop theory for using heuristics to solve computationally hard problems in differential
privacy. Heuristic approaches have enjoyed tremendous success in machine learning, for …

Fair machine learning has become a significant research topic with broad societal impact.
However, most fair learning methods require direct access to personal demographic data …

We investigate and leverage a connection between Differential Privacy (DP) and the
recently proposed notion of Distributional Generalization (DG). Applying this connection, we …

Differentially private machine learning trains models while protecting privacy of the sensitive
training data. The key to obtain differentially private models is to introduce …

Deep learning's unprecedented success raises several ethical concerns ranging from
biased predictions to data privacy. Researchers tackle these issues by introducing fairness …

Recent works have shown that selecting an optimal model architecture suited to the
differential privacy setting is necessary to achieve the best possible utility for a given privacy …

We derive the optimal differential privacy (DP) parameters of a mechanism that satisfies a
given level of Renyí differential privacy (RDP). Our result is based on the joint range of two f …