Yves-Alexandre de Montjoye — accepted AI-conference papers

2025

Certification for Differentially Private Prediction in Gradient-Based Training

Matthew Robert Wicker, Philip Sosnin, Igor Shilov, Adrianna Janik, Mark Niklas Mueller, Yves-Alexandre de Montjoye +2

ICML 2025poster

We study private prediction where differential privacy is achieved by adding noise to the outputs of a non-private model. Existing methods rely on noise proportional to the global sensitivity of the model, often resulting in sub-optimal privacy-utility trade-offs compared to private training. We int…

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2025

Exploring the limits of strong membership inference attacks on large language models

Jamie Hayes, Ilia Shumailov, Christopher A. Choquette-Choo, Matthew Jagielski, Georgios Kaissis, Milad Nasr +9

NeurIPS 2025poster

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 models (LLMs). As a result, prior research has either relied on weaker attacks that avoid training references (e.g., fine-tu…

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2024

Copyright Traps for Large Language Models

Matthieu Meeus, Igor Shilov, Manuel Faysse, Yves-Alexandre de Montjoye

ICML 2024poster

Questions of fair use of copyright-protected content to train Large Language Models (LLMs) are being actively debated. Document-level inference has been proposed as a new task: inferring from black-box access to the trained model whether a piece of content has been seen during training. SOTA methods…

Cited by 30 Source ↗PDF ↗code ↗Scholar ↗

2019

Differentially Private Compressive K-means

Vincent Schellekens, Antoine Chatalic, Florimond Houssiau, Yves-Alexandre de Montjoye, Laurent Jacques, Rémi Gribonval

ICASSP 2019accepted

This work addresses the problem of learning from large collections of data with privacy guarantees. The sketched learning framework proposes to deal with the large scale of datasets by compressing them into a single vector of generalized random moments, from which the learning task is then performed…

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