Machine learning algorithms, when applied to sensitive data, pose a distinct threat to
privacy. A growing body of prior work demonstrates that models produced by these …

Membership inference (MI) attacks exploit the fact that machine learning algorithms
sometimes leak information about their training data through the learned model. In this work …

In this paper we study differentially private Empirical Risk Minimization (ERM) in different
settings. For smooth (strongly) convex loss function with or without (non)-smooth …

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 …

Differential privacy has emerged as an important standard for privacy preserving
computation over databases containing sensitive information about individuals. Research …

Federated Learning (FL) is a new computing paradigm in privacy-preserving Machine
Learning (ML), where the ML model is trained in a decentralized manner by the clients …

Federal statistics agencies strive to release data products that are informative for many
purposes, yet also protect the privacy and confidentiality of data subjects' identities and …

Abstract Machine learning algorithms, when applied to sensitive data, pose a distinct threat
to privacy. A growing body of prior work demonstrates that models produced by these …

Many data producers seek to provide users access to confidential data without unduly
compromising data subjects' privacy and confidentiality. One general strategy is to require …

Abstract Machine learning models, especially deep neural networks are known to be
susceptible to privacy attacks such as membership inference where an adversary can detect …