Graph neural networks (GNNs) are an emerging research field. This specialized deep
neural network architecture is capable of processing graph structured data and bridges the …

Machine learning (ML) models have been widely applied to various applications, including
image classification, text generation, audio recognition, and graph data analysis. However …

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 …

Large language models (LMs) have been shown to memorize parts of their training data,
and when prompted appropriately, they will emit the memorized training data verbatim. This …

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 …

This paper studies extractable memorization: training data that an adversary can efficiently
extract by querying a machine learning model without prior knowledge of the training …

It has become common to publish large (billion parameter) language models that have been
trained on private datasets. This paper demonstrates that in such settings, an adversary can …

Generative Adversarial Networks (GANs) have promoted a variety of applications in
computer vision and natural language processing, among others, due to its generative …

Understanding to what extent neural networks memorize training data is an intriguing
question with practical and theoretical implications. In this paper we show that in some …

Differential Privacy (DP) provides a formal privacy guarantee preventing adversaries with
access to a machine learning model from extracting information about individual training …