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) …

Text-to-image models suffer from various safety issues that may limit their suitability for
deployment. Previous methods have separately addressed individual issues of bias …

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 …

Machine learning models may inadvertently memorize sensitive, unauthorized, or malicious
data, posing risks of privacy breaches, security vulnerabilities, and performance …

With the introduction of data protection and privacy regulations, it has become crucial to
remove the lineage of data on demand from a machine learning (ML) model. In the last few …

The current pace of development and applications of large language models (LLMs) is
unprecedented and will impact future medical care significantly. In this critical review, we …

Abstract Model distillation is frequently proposed as a technique to reduce the privacy
leakage of machine learning. These empirical privacy defenses rely on the intuition that …

The rapid expansion of large-scale text-to-image diffusion models has raised growing
concerns regarding their potential misuse in creating harmful or misleading content. In this …

Membership inference (MI) attacks mainly aim to infer whether a data record was used to
train a target model or not. Due to the serious privacy risks, MI attacks have been attracting a …

Synthetic data is seen as the most promising solution to share individual-level data while
preserving privacy. Shadow modeling-based Membership Inference Attacks (MIAs) have …