6 citations · 10 across the 10 of their papers we have counts for
5 papers · 1 filter
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…
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…
P2NIA: Privacy-Preserving Non-Iterative Auditing
Jade Garcia Bourrée, Hadrien Lautraite, Sébastien Gambs +3
The emergence of AI legislation has increased the need to assess the ethical compliance of high-risk AI systems. Traditional auditing methods rely on platforms' application program…
Training Set Reconstruction from Differentially Private Forests: How Effective is DP?
Alice Gorgé, Julien Ferry, Sébastien Gambs +1
Recent research has shown that structured machine learning models such as tree ensembles are vulnerable to privacy attacks targeting their training data. To mitigate these risks, d…
WaKA: Data Attribution using K-Nearest Neighbors and Membership Privacy Principles
Patrick Mesana, Clément Bénesse, Hadrien Lautraite +2
In this paper, we introduce WaKA (Wasserstein K-nearest-neighbors Attribution), a novel attribution method that leverages principles from the LiRA (Likelihood Ratio Attack) framewo…