Differential privacy is a strong notion for privacy that can be used to prove formal
guarantees, in terms of a privacy budget, ε, about how much information is leaked by a …

Convex empirical risk minimization is a basic tool in machine learning and statistics. We
provide new algorithms and matching lower bounds for differentially private convex …

Differential privacy is an essential and prevalent privacy model that has been widely
explored in recent decades. This survey provides a comprehensive and structured overview …

We present one-shot federated learning, where a central server learns a global model over
a network of federated devices in a single round of communication. Our approach-drawing …

Distributed learning allows a group of independent data owners to collaboratively learn a
model over their data sets without exposing their private data. We present a distributed …

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 …

We study membership inference in settings where some of the assumptions typically used in
previous research are relaxed. First, we consider skewed priors, to cover cases such as …

Building useful predictive models often involves learning from sensitive data. Training
models with differential privacy can guarantee the privacy of such sensitive data. For convex …

While significant progress has been made separately on analytics systems for scalable
stochastic gradient descent (SGD) and private SGD, none of the major scalable analytics …

Big data is currently a hot research topic, with four million hits on Google scholar in October
2016. One reason for the popularity of big data research is the knowledge that can be …