Artificial intelligence (AI) methods have multiplied our capabilities to extract quantitative
information from digital histopathology images. AI is expected to reduce workload for human …

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

Denoising diffusion probabilistic models are becoming the leading generative modeling
paradigm for many important data modalities. Being the most prevalent in the computer …

The rapid advancement and widespread use of large language models (LLMs) have raised
significant concerns regarding the potential leakage of personally identifiable information …

Recent work argues that robust training requires substantially larger datasets than those
required for standard classification. On CIFAR-10 and CIFAR-100, this translates into a …

Diffusion-based generative models have shown great potential for image synthesis, but
there is a lack of research on the security and privacy risks they may pose. In this paper, we …

This explainer document aims to provide an overview of the current state of the rapidly
expanding work on synthetic data technologies, with a particular focus on privacy. The …

Machine learning heavily relies on data, but real-world applications often encounter various
data-related issues. These include data of poor quality, insufficient data points leading to …

Abstract Empirical attacks on Federated Learning (FL) systems indicate that FL is fraught
with numerous attack surfaces throughout the FL execution. These attacks can not only …

Federated Learning (FL) framework brings privacy benefits to distributed learning systems
by allowing multiple clients to participate in a learning task under the coordination of a …