In recent years, privacy and security concerns in machine learning have promoted trusted
federated learning to the forefront of research. Differential privacy has emerged as the de …

Large Language Models (LLMs) are advancing at a remarkable pace, with myriad
applications under development. Unlike most earlier machine learning models, they are no …

Privacy and security challenges in Machine Learning (ML) have become increasingly
severe, along with ML's pervasive development and the recent demonstration of large attack …

Machine unlearning aims to remove points from the training dataset of a machine learning
model after training; for example when a user requests their data to be deleted. While many …

Abstract This NIST Trustworthy and Responsible AI report develops a taxonomy of concepts
and defines terminology in the field of adversarial machine learning (AML). The taxonomy is …

Stochastic Gradient Descent (SGD) is a popular training algorithm, a cornerstone of modern
machine learning systems. Several security applications benefit from determining if SGD …

Machine unlearning refers to the process of mitigating the influence of specific training data
on machine learning models based on removal requests from data owners. However, one …

In this article, we present a detailed review of current practices and state-of-the-art
methodologies in the field of differential privacy (DP), with a focus of advancing DP's …

This paper details the privacy and security landscape in today's cloud ecosystem and
identifies that there is a gap in addressing the risks introduced by machine learning models …

Aiming to train and deploy predictive models, organizations collect large amounts of detailed
client data, risking the exposure of private information in the event of a breach. To mitigate …