Igor Shilov

According to our database, Igor Shilov authored at least 11 papers between 2021 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

Counterfactual Influence as a Distributional Quantity.

Matthieu Meeus, Igor Shilov, Georgios Kaissis, Yves-Alexandre de Montjoye

CoRR, June, 2025

Strong Membership Inference Attacks on Massive Datasets and (Moderately) Large Language Models.

Jamie Hayes, Ilia Shumailov, Christopher A. Choquette-Choo, Matthew Jagielski, Georgios Kaissis, Katherine Lee, Milad Nasr, Sahra Ghalebikesabi, Niloofar Mireshghallah, Meenatchi Sundaram Mutu Selva Annamalai, Igor Shilov, Matthieu Meeus, Yves-Alexandre de Montjoye, Franziska Boenisch, Adam Dziedzic, A. Feder Cooper

CoRR, May, 2025

SoK: Membership Inference Attacks on LLMs are Rushing Nowhere (and How to Fix It).

Matthieu Meeus, Igor Shilov, Shubham Jain, Manuel Faysse, Marek Rei, Yves-Alexandre de Montjoye

Proceedings of the IEEE Conference on Secure and Trustworthy Machine Learning, 2025

2024

Sub-optimal Learning in Meta-Classifier Attacks: A Study of Membership Inference on Differentially Private Location Aggregates.

Yuhan Liu, Florent Guépin, Igor Shilov, Yves-Alexandre de Montjoye

CoRR, 2024

Watermarking Training Data of Music Generation Models.

Pascal Epple, Igor Shilov, Bozhidar Stevanoski, Yves-Alexandre de Montjoye

CoRR, 2024

Free Record-Level Privacy Risk Evaluation Through Artifact-Based Methods.

Joseph Pollock, Igor Shilov, Euodia Dodd, Yves-Alexandre de Montjoye

CoRR, 2024

Mosaic Memory: Fuzzy Duplication in Copyright Traps for Large Language Models.

Igor Shilov, Matthieu Meeus, Yves-Alexandre de Montjoye

CoRR, 2024

Copyright Traps for Large Language Models.

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

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2022

Defending against Reconstruction Attacks with Rényi Differential Privacy.

Pierre Stock, Igor Shilov, Ilya Mironov, Alexandre Sablayrolles

CoRR, 2022

2021

Opacus: User-Friendly Differential Privacy Library in PyTorch.

Ashkan Yousefpour, Igor Shilov, Alexandre Sablayrolles, Davide Testuggine, Karthik Prasad, Mani Malek, John Nguyen, Sayan Ghosh, Akash Bharadwaj, Jessica Zhao, Graham Cormode, Ilya Mironov

CoRR, 2021

Antipodes of Label Differential Privacy: PATE and ALIBI.

Mani Malek Esmaeili, Ilya Mironov, Karthik Prasad, Igor Shilov, Florian Tramèr

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021