Language Models (LMs) have been shown to leak information about training data through
sentence-level membership inference and reconstruction attacks. Understanding the risk of …

Federated Learning (FL) allows multiple participants to train machine learning models
collaboratively by keeping their datasets local while only exchanging model updates. Alas …

Membership inference attacks are a key measure to evaluate privacy leakage in machine
learning (ML) models. It is important to train ML models that have high membership privacy …

Deploying machine learning models in production may allow adversaries to infer sensitive
information about training data. There is a vast literature analyzing different types of …

Alzheimer's disease is a neurodegenerative disorder and the most common form of
dementia. Early diagnosis may assist interventions to delay onset and reduce the …

Abstract Algorithms such as Differentially Private SGD enable training machine learning
models with formal privacy guarantees. However, because these guarantees hold with …

Recent advanced high-throughput field phenotyping combined with sophisticated big data
analysis methods have provided plant breeders with unprecedented tools for a better …

In a model inversion attack, an adversary attempts to reconstruct the training data records of
a target model using only the model's output. In launching a contemporary model inversion …

Leakage of data from publicly available Machine Learning (ML) models is an area of
growing significance since commercial and government applications of ML can draw on …

Online fault detection of insulators is a necessary requirement for the development of a
smart grid, which directly affects the safety and reliability of power system operations …