Federated learning (FL) is a machine learning setting where many clients (eg, mobile
devices or whole organizations) collaboratively train a model under the orchestration of a …

Differential privacy is often studied in one of two models. In the central model, a single
analyzer has the responsibility of performing a privacy-preserving computation on data. But …

It is well-known that general secure multi-party computation can in principle be applied to
implement differentially private mechanisms over distributed data with utility matching the …

The shuffle model of differential privacy has attracted attention in the literature due to it being
a middle ground between the well-studied central and local models. In this work, we study …

We provide an approximation algorithm for k-means clustering in the\emph {one-
round}(aka\emph {non-interactive}) local model of differential privacy (DP). Our algorithm …

Reinforcement learning algorithms are widely used in domains where it is desirable to
provide a personalized service. In these domains it is common that user data contains …

Differential privacy (DP) is a formal notion for quantifying the privacy loss of algorithms.
Algorithms in the central model of DP achieve high accuracy but make the strongest trust …

Contextual bandit algorithms are widely used in domains where it is desirable to provide a
personalized service by leveraging contextual information, that may contain sensitive …

There has been a recent wave of interest in intermediate trust models for differential privacy
that eliminate the need for a fully trusted central data collector, but overcome the limitations …

While learning in an unknown Markov Decision Process (MDP), an agent should trade off
exploration to discover new information about the MDP, and exploitation of the current …