Differential privacy comes equipped with multiple analytical tools for the design of private
data analyses. One important tool is the so-called" privacy amplification by subsampling" …

We consider the problem of privacy-amplification by under the Renyi Differential Privacy
framework. This is the main technique underlying the moments accountants (Abadi et al …

Leakage of data from publicly available Machine Learning (ML) models is an area of
growing significance since commercial and government applications of ML can draw on …

With increasing ethical and legal concerns on privacy for deep models in visual recognition,
differential privacy has emerged as a mechanism to disguise membership of sensitive data …

Linear regression is an important tool across many fields that work with sensitive human-
sourced data. Significant prior work has focused on producing differentially private point …

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 …

Cross-collection topic models extend previous single-collection topic models, such as Latent
Dirichlet Allocation (LDA), to multiple collections. The purpose of cross-collection topic …

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 …

Standard Bayesian inference algorithms are prohibitively expensive in the regime of modern
large-scale data. Recent work has found that a small, weighted subset of data (a coreset) …

Federated learning (FL) has been recognized as a promising collaborative on-device
machine learning method in the design of Internet of Things (IoT) systems. However, most …