Deep Learning (DL) algorithms based on artificial neural networks have achieved
remarkable success and are being extensively applied in a variety of application domains …

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 …

Recent works have shown that Generative Adversarial Networks (GANs) may generalize
poorly and thus are vulnerable to privacy attacks. In this paper, we seek to improve the …

The ever-growing advances of deep learning in many areas including vision,
recommendation systems, natural language processing, etc., have led to the adoption of …

In recent years, the research community has increasingly focused on understanding the
security and privacy challenges posed by deep learning models. However, the security …

Machine-learning (ML) algorithms are increasingly utilized in privacy-sensitive applications
such as predicting lifestyle choices, making medical diagnoses, and facial recognition. In a …

Machine learning models are vulnerable to data inference attacks, such as membership
inference and model inversion attacks. In these types of breaches, an adversary attempts to …

Deep learning models often raise privacy concerns as they leak information about their
training data. This leakage enables membership inference attacks (MIA) that can identify …

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 …