Material Classification from Imprecise Chemical Composition : Probabilistic vs Possibilistic Approach. - Arnaud Grivet Sébert; Jean-Philippe Poli (FUZZ-IEEE, 2018)

Fuzzy Rule Learning for Material Classification from Imprecise Data. - Arnaud Grivet Sébert; Jean-Philippe Poli (IPMU (1), 2018)

Preference Aggregation in the Generalised Unavailable Candidate Model. - Arnaud Grivet Sébert; Nicolas Maudet; Patrice Perny; Paolo Viappiani (ADT, 2021)

Rank Aggregation by Dissatisfaction Minimisation in the Unavailable Candidate Model. - Arnaud Grivet Sébert; Nicolas Maudet; Patrice Perny; Paolo Viappiani (AAMAS, 2021)

SPEED: secure, PrivatE, and efficient deep learning. - Arnaud Grivet Sébert; Rafael Pinot; Martin Zuber; Cédric Gouy-Pailler; Renaud Sirdey (Mach. Learn., 2021)

Protecting Data from all Parties: Combining FHE and DP in Federated Learning. - Arnaud Grivet Sébert; Renaud Sirdey; Oana Stan; Cédric Gouy-Pailler (CoRR, 2022)

Combining homomorphic encryption and differential privacy in federated learning. - Arnaud Grivet Sébert; Marina Checri; Oana Stan; Renaud Sirdey; Cédric Gouy-Pailler (PST, 2023)

A Probabilistic Design for Practical Homomorphic Majority Voting with Intrinsic Differential Privacy. - Arnaud Grivet Sébert; Martin Zuber; Oana Stan; Renaud Sirdey; Cédric Gouy-Pailler (WAHC@CCS, 2023)

When approximate design for fast homomorphic computation provides differential privacy guarantees. - Arnaud Grivet Sébert; Martin Zuber; Oana Stan; Renaud Sirdey; Cédric Gouy-Pailler (CoRR, 2023)

Combining differential privacy and homomorphic encryption for privacy-preserving collaborative machine learning. (Approches combinant confidentialité différentielle et chiffrement homomorphe pour la protection des données en apprentissage automatique collaboratif). - Arnaud Grivet Sébert (2023)