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 …

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 …

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 …

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 …

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 …

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 …

Machine learning models leak significant amount of information about their training sets,
through their predictions. This is a serious privacy concern for the users of machine learning …

An important problem in deep learning is the privacy and security of neural networks (NNs).
Both aspects have long been considered separately. To date, it is still poorly understood …

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 …

The newly emerged machine learning (eg, deep learning) methods have become a strong
driving force to revolutionize a wide range of industries, such as smart healthcare, financial …