24 citations · 42 across the 14 of their papers we have counts for
4 papers · 1 filter
Counterfactual Influence as a Distributional Quantity
Matthieu Meeus, Igor Shilov, Georgios Kaissis +1
Machine learning models are known to memorize samples from their training data, raising concerns around privacy and generalization. Counterfactual self-influence is a popular metri…
Exploring the limits of strong membership inference attacks on large language models
Jamie Hayes, Ilia Shumailov, Christopher A. Choquette-Choo +13
State-of-the-art membership inference attacks (MIAs) typically require training many reference models, making it difficult to scale these attacks to large pre-trained language mode…
Auditing Prompt Injection Defenses Before They Reach Users: What Strong Adversaries Reveal
Xiaoxue Yang, Bozhidar Stevanoski, Matthieu Meeus +1
Large language models are increasingly deployed in user-facing products that handle sensitive personal data while interacting with untrusted external content. This makes them a pri…
The Canary's Echo: Auditing Privacy Risks of LLM-Generated Synthetic Text
Matthieu Meeus, Lukas Wutschitz, Santiago Zanella-Béguelin +2
How much information about training samples can be leaked through synthetic data generated by Large Language Models (LLMs)? Overlooking the subtleties of information flow in synthe…