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 …

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 …

Today, computer systems hold large amounts of personal data. Yet while such an
abundance of data allows breakthroughs in artificial intelligence, and especially machine …

Membership inference is one of the simplest privacy threats faced by machine learning
models that are trained on private sensitive data. In this attack, an adversary infers whether a …

Inference attacks against Machine Learning (ML) models allow adversaries to learn
sensitive information about training data, model parameters, etc. While researchers have …

Abstract Machine learning (ML) methods have demonstrated impressive performance in
many application fields such as autopilot, facial recognition, and spam detection …

Abstract The Right to be Forgotten is part of the recently enacted General Data Protection
Regulation (GDPR) law that affects any data holder that has data on European Union …

The privacy risks of machine learning models is a major concern when training them on
sensitive and personal data. We discuss the tradeoffs between data privacy and the …

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

Nowadays, deep learning is becoming increasingly important in our daily life. The
appearance of deep learning in many applications in life relates to prediction and …