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 …

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 …

When building machine learning models using sensitive data, organizations should ensure
that the data processed in such systems is adequately protected. For projects involving …

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 …

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 …

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 …

Most current approaches for protecting privacy in machine learning (ML) assume that
models exist in a vacuum, when in reality, ML models are part of larger systems that include …

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 …

Data is the key factor to drive the development of machine learning (ML) during the past
decade. However, high-quality data, in particular labeled data, is often hard and expensive …

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 …