11 citations · 18 across the 3 of their papers we have counts for
3 papers
cs.LG2022★ 2 cited
On the Alignment of Group Fairness with Attribute Privacy
Jan Aalmoes, Vasisht Duddu, Antoine Boutet
Group fairness and privacy are fundamental aspects in designing trustworthy machine learning models. Previous research has highlighted conflicts between group fairness and differen…
cs.CR2022★ 5 cited
Dikaios: Privacy Auditing of Algorithmic Fairness via Attribute Inference Attacks
Jan Aalmoes, Vasisht Duddu, Antoine Boutet
Machine learning (ML) models have been deployed for high-stakes applications. Due to class imbalance in the sensitive attribute observed in the datasets, ML models are unfair on mi…
cs.LG2021★ 11 cited
MixNN: Protection of Federated Learning Against Inference Attacks by Mixing Neural Network Layers
Antoine Boutet, Thomas Lebrun, Jan Aalmoes +1
Machine Learning (ML) has emerged as a core technology to provide learning models to perform complex tasks. Boosted by Machine Learning as a Service (MLaaS), the number of applicat…