With the rapid development of low-cost consumer electronics and pervasive adoption of next
generation wireless communication technologies, a tremendous amount of data has been …

Collecting and analyzing massive data generated from smart devices have become
increasingly pervasive in crowdsensing, which are the building blocks for data-driven …

In this work, we consider the optimization formulation of personalized federated learning
recently introduced by Hanzely & Richtarik (2020) which was shown to give an alternative …

Deep Neural Networks (DNN) are vulnerable to adversarial samples that are generated by
perturbing correctly classified inputs to cause DNN models to misbehave (eg …

We study differentially private (DP) algorithms for stochastic convex optimization (SCO). In
this problem the goal is to approximately minimize the population loss given iid~ samples …

We study differentially private (DP) algorithms for stochastic convex optimization: the
problem of minimizing the population loss given iid samples from a distribution over convex …

Matrix factorization (MF) mechanisms for differential privacy (DP) have substantially
improved the state-of-the-art in privacy-utility-computation tradeoffs for ML applications in a …

The notion of Local Differential Privacy (LDP) enables users to respond to sensitive
questions while preserving their privacy. The basic LDP frequent oracle (FO) protocol …

Motivated by recent applications requiring differential privacy in the setting of adaptive
streams, we investigate the question of optimal instantiations of the matrix mechanism in this …

Local differential privacy (LDP), where each user perturbs her data locally before sending to
an untrusted data collector, is a new and promising technique for privacy-preserving …