Search Results for author: Matthieu Meeus

Found 4 papers, 0 papers with code

Copyright Traps for Large Language Models

no code implementations14 Feb 2024 Matthieu Meeus, Igor Shilov, Manuel Faysse, Yves-Alexandre de Montjoye

We here propose to use copyright traps, the inclusion of fictitious entries in original content, to detect the use of copyrighted materials in LLMs with a focus on models where memorization does not naturally occur.


Did the Neurons Read your Book? Document-level Membership Inference for Large Language Models

no code implementations23 Oct 2023 Matthieu Meeus, Shubham Jain, Marek Rei, Yves-Alexandre de Montjoye

First, we propose a procedure for the development and evaluation of document-level membership inference for LLMs by leveraging commonly used data sources for training and the model release date.

Misinformation Sentence

Synthetic is all you need: removing the auxiliary data assumption for membership inference attacks against synthetic data

no code implementations4 Jul 2023 Florent Guépin, Matthieu Meeus, Ana-Maria Cretu, Yves-Alexandre de Montjoye

While membership inference attacks (MIAs), based on shadow modeling, have become the standard to evaluate the privacy of synthetic data, they currently assume the attacker to have access to an auxiliary dataset sampled from a similar distribution as the training dataset.

Achilles' Heels: Vulnerable Record Identification in Synthetic Data Publishing

no code implementations17 Jun 2023 Matthieu Meeus, Florent Guépin, Ana-Maria Cretu, Yves-Alexandre de Montjoye

The choice of vulnerable records is as important as more accurate MIAs when evaluating the privacy of synthetic data releases, including from a legal perspective.

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