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

Transformer-based pretrained language models (T-PTLMs) have achieved great success in
almost every NLP task. The evolution of these models started with GPT and BERT. These …

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 …

Language Models (LMs) often cannot be deployed because of their potential to harm users
in hard-to-predict ways. Prior work identifies harmful behaviors before deployment by using …

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

As machine learning becomes more widely used, the need to study its implications in
security and privacy becomes more urgent. Although the body of work in privacy has been …

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 …

Face obfuscation (blurring, mosaicing, etc.) has been shown to be effective for privacy
protection; nevertheless, object recognition research typically assumes access to complete …

The increasing popularity of large language models (LLMs) such as ChatGPT has led to
growing concerns about their safety, security risks, and ethical implications. This paper aims …

The wide adoption and application of Masked language models~(MLMs) on sensitive data
(from legal to medical) necessitates a thorough quantitative investigation into their privacy …