6 citations · 10 across the 10 of their papers we have counts for
4 papers · 1 filter
Neighborhood Watch: Privacy Risks in Seeded Local Combination Synthetic Data
Hadrien Lautraite, Tristan Allard, Anne-Sophie Charest +2
Synthetic data is seen as a promising solution for sharing data in sensitive contexts. However, recent work on privacy attacks have shown that there are still significant residual…
Manipulation-Proof Oblivious Audits against Deceptive Model Providers
Augustin Godinot, Sofiane Azogagh, Julien Ferry +1
Audits have emerged as a critical instrument for algorithmic governance, providing a mechanism for external scrutiny and governance of machine learning models. However, ensuring th…
Embedding Inference Attack
Cedric Fitiavana Raelijohn, Sébastien Gambs, Jean-Francois Rajotte
Embedding models are essential components of modern Information Retrieval (IR) systems, yet they are typically hidden behind APIs. Recent works have shown that dense IR system can…
Quantifying the Privacy of Counterfactuals by Leveraging Membership Inference Attacks Against Synthetic Data
Maryam Babaei, Yingke Wang, Hadrien Lautraite +3
Counterfactuals are typically used in high-stakes decision areas to explain a machine learning model by showing how changes to the user profiles result in the desired outcome. Howe…