The goal of privacy metrics is to measure the degree of privacy enjoyed by users in a system
and the amount of protection offered by privacy-enhancing technologies. In this way, privacy …

The vast majority of artificial intelligence solutions are founded on game theory, and
differential privacy is emerging as perhaps the most rigorous and widely adopted privacy …

The problem of privacy-preserving data analysis has a long history spanning multiple
disciplines. As electronic data about individuals becomes increasingly detailed, and as …

Many resource allocation problems can be formulated as an optimization problem whose
constraints contain sensitive information about participating users. This paper concerns a …

Federated learning is a promising distributed machine learning paradigm that has been
playing a significant role in providing privacy-preserving learning solutions. However …

Federated learning (FL) is a training paradigm where the clients collaboratively learn
models by repeatedly sharing information without compromising much on the privacy of their …

We study personalization of supervised learning with user-level differential privacy.
Consider a setting with many users, each of whom has a training data set drawn from their …

Motivated by high-stakes decision-making domains like personalized medicine where user
information is inherently sensitive, we design privacy preserving exploration policies for …

We generalize the continuous observation privacy setting from Dwork et al. and Chan et al.
by allowing each event in a stream to be a subset of some (possibly unknown) universe of …

We study the quality of outcomes in repeated games when the population of players is
dynamically changing, and where participants use learning algorithms to adapt to the …