47 citations · 214 across the 25 of their papers we have counts for
27 papers
Smooth Sensitivity for Learning Differentially-Private yet Accurate Rule Lists
Timothée Ly, Julien Ferry, Marie-José Huguet +2
Differentially-private (DP) mechanisms can be embedded into the design of a machine learning algorithm to protect the resulting model against privacy leakage. However, this often c…
SoK: Taming the Triangle -- On the Interplays between Fairness, Interpretability and Privacy in Machine Learning
Julien Ferry, Ulrich Aïvodji, Sébastien Gambs +2
Machine learning techniques are increasingly used for high-stakes decision-making, such as college admissions, loan attribution or recidivism prediction. Thus, it is crucial to ens…
Crypto'Graph: Leveraging Privacy-Preserving Distributed Link Prediction for Robust Graph Learning
Sofiane Azogagh, Zelma Aubin Birba, Sébastien Gambs +1
Graphs are a widely used data structure for collecting and analyzing relational data. However, when the graph structure is distributed across several parties, its analysis is parti…
Probabilistic Dataset Reconstruction from Interpretable Models
Julien Ferry, Ulrich Aïvodji, Sébastien Gambs +2
Interpretability is often pointed out as a key requirement for trustworthy machine learning. However, learning and releasing models that are inherently interpretable leaks informat…
Membership Inference Attack for Beluga Whales Discrimination
Voncarlos Marcelo Araújo, Sébastien Gambs, Clément Chion +3
To efficiently monitor the growth and evolution of a particular wildlife population, one of the main fundamental challenges to address in animal ecology is the re-identification of…
Frequency Estimation of Evolving Data Under Local Differential Privacy
Héber H. Arcolezi, Carlos Pinzón, Catuscia Palamidessi +1
Collecting and analyzing evolving longitudinal data has become a common practice. One possible approach to protect the users' privacy in this context is to use local differential p…