As machine learning becomes more widely used, the need to study its implications in
security and privacy becomes more urgent. Although the body of work in privacy has been …

This article reviews privacy challenges in machine learning and provides a critical overview
of the relevant research literature. The possible adversarial models are discussed, a wide …

Over the past few years, providers such as Google, Microsoft, and Amazon have started to
provide customers with access to software interfaces allowing them to easily embed …

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 …

The arms race between attacks and defenses for machine learning models has come to a
forefront in recent years, in both the security community and the privacy community …

For privacy concerns to be addressed adequately in today's machine-learning (ML) systems,
the knowledge gap between the ML and privacy communities must be bridged. This article …

Machine learning has been pervasively used in a wide range of applications due to its
technical breakthroughs in recent years. It has demonstrated significant success in dealing …

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 …

Machine learning models are prone to memorizing sensitive data, making them vulnerable
to membership inference attacks in which an adversary aims to guess if an input sample was …

Deploying machine learning models in production may allow adversaries to infer sensitive
information about training data. There is a vast literature analyzing different types of …