Differential privacy guarantees allow the results of a statistical analysis involving sensitive
data to be released without compromising the privacy of any individual taking part …

Generalized linear models (GLMs) such as logistic regression are among the most widely
used arms in data analyst's repertoire and often used on sensitive datasets. A large body of …

The pandemic in 2020 and 2021 had enormous economic and societal consequences, and
studies show that contact-tracing algorithms can be key in the early containment of the virus …

While generation of synthetic data under differential privacy (DP) has received a lot of
attention in the data privacy community, analysis of synthetic data has received much less …

Bayesian inference provides a principled framework for learning from complex data and
reasoning under uncertainty. It has been widely applied in machine learning tasks such as …

Recent developments in differentially private (DP) machine learning and DP Bayesian
learning have enabled learning under strong privacy guarantees for the training data …

Combining several (sample approximations of) distributions, which we term sub-posteriors,
into a single distribution proportional to their product, is a common challenge. Occurring, for …

There has been considerable interest in addressing the problem of unifying distributed
analyses into a single coherent inference, which arises in big-data settings, when working …

This paper concerns differentially private Bayesian estimation of the parameters of a
population distribution, when a noisy statistic of a sample from that population is shared to …

With the rapid increase in the use and deployment of machine learning (ML) systems in the
world, concomitant concerns on the ethical implications of their downstream effect have …