4 papers
CheckMIABench: Firm Foundations For Membership Inference Attacks on Language Models
Jeffrey G. Wang, Jason Wang, Marvin Li +1
Membership inference attacks (MIAs) are a canonical way to assess a machine learning model's privacy properties. Although several attempts have been made to evaluate MIAs on langua…
Pandora's White-Box: Precise Training Data Detection and Extraction in Large Language Models
Jeffrey G. Wang, Jason Wang, Marvin Li +1
In this paper we develop state-of-the-art privacy attacks against Large Language Models (LLMs), where an adversary with some access to the model tries to learn something about the…
MoPe: Model Perturbation-based Privacy Attacks on Language Models
Marvin Li, Jason Wang, Jeffrey Wang +1
Recent work has shown that Large Language Models (LLMs) can unintentionally leak sensitive information present in their training data. In this paper, we present Model Perturbations…
Black-Box Training Data Identification in GANs via Detector Networks
Lukman Olagoke, Salil Vadhan, Seth Neel
Since their inception Generative Adversarial Networks (GANs) have been popular generative models across images, audio, video, and tabular data. In this paper we study whether given…