Machine learning (ML) has gained widespread adoption in a variety of fields, including
computer vision and natural language processing. However, ML models are vulnerable to …

Image diffusion models such as DALL-E 2, Imagen, and Stable Diffusion have attracted
significant attention due to their ability to generate high-quality synthetic images. In this work …

A membership inference attack allows an adversary to query a trained machine learning
model to predict whether or not a particular example was contained in the model's training …

Ensuring alignment, which refers to making models behave in accordance with human
intentions [1, 2], has become a critical task before deploying large language models (LLMs) …

Membership Inference attacks (MIAs) aim to predict whether a data sample was present in
the training data of a machine learning model or not, and are widely used for assessing the …

We introduce a new class of attacks on machine learning models. We show that an
adversary who can poison a training dataset can cause models trained on this dataset to …

With the rapid development of artificial intelligence, large language models (LLMs) have
made remarkable advancements in natural language processing. These models are trained …

The wide adoption and application of Masked language models~(MLMs) on sensitive data
(from legal to medical) necessitates a thorough quantitative investigation into their privacy …

Empirical defenses for machine learning privacy forgo the provable guarantees of
differential privacy in the hope of achieving higher utility while resisting realistic adversaries …

Machine learning models are vulnerable to membership inference attacks in which an
adversary aims to predict whether or not a particular sample was contained in the target …