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 …

The wide adoption of Machine Learning to solve a large set of real-life problems came with
the need to collect and process large volumes of data, some of which are considered …

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 …

Machine learning (ML) is increasingly being adopted in a wide variety of application
domains. Usually, a well-performing ML model relies on a large volume of training data and …

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 …

The exponential growth of big data and deep learning has increased the data exchange
traffic in society. Machine Learning as a Service,(MLaaS) which leverages deep learning …

Privacy-preserving machine learning (PPML) via Secure Multi-party Computation (MPC) has
gained momentum in the recent past. Assuming a minimal network of pair-wise private …

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 …

Machine learning algorithms based on deep Neural Networks (NN) have achieved
remarkable results and are being extensively used in different domains. On the other hand …

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 …