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 …

Artificial intelligence (AI) has witnessed a substantial breakthrough in a variety of Internet of
Things (IoT) applications and services, spanning from recommendation systems and speech …

Deep neural networks are susceptible to various inference attacks as they remember
information about their training data. We design white-box inference attacks to perform a …

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 …

The prevalence of deep learning has drawn attention to the privacy protection of sensitive
data. Various privacy threats have been presented, where an adversary can steal model …

Machine learning has become mainstream across industries. Numerous examples prove the
validity of it for security applications. In this work, we investigate how to reverse engineer a …

Deep neural networks are susceptible to various inference attacks as they remember
information about their training data. We design white-box inference attacks to perform a …

Machine learning (ML) models are applied in an increasing variety of domains. The
availability of large amounts of data and computational resources encourages the …

Currently, machine learning (ML) techniques are at the heart of smart cyber-physical
systems (CPSs) and Internet-of-Things (loT). This article discusses various challenges and …

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 …