Privacy preserving and secure machine learning (PPSML), has turned into increasingly
significant in recent years due to the need to protect sensitive data while enabling data …

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 …

As an implementation methodology of the artificial intelligence, machine learning techniques
have reported impressive performance in a variety of application domains, such as risk …

As machine learning models become increasingly ubiquitous, ensuring privacy protection
has emerged as a critical concern. This paper presents an in-depth exploration of privacy …

The area of privacy preserving machine learning has been of growing importance in
practice, which has lead to an increased interest in this topic in both academia and industry …

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 …

This paper examines the evolving landscape of machine learning (ML) and its profound
impact across various sectors, with a special focus on the emerging field of Privacy …

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 …

Abstract Machine learning algorithms are proven to be vulnerable to model inversion and
membership inference attacks, which raises much privacy concerns for its applications in …

Keep sensitive user data safe and secure without sacrificing the performance and accuracy
of your machine learning models. In Privacy Preserving Machine Learning, you will learn …