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Fuzzy rule learning for material classification from imprecise data

Published in Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Foundations: 17th International Conference (IPMU 2018), 2018

Recommended citation: Grivet Sébert, A., & Poli, J. P. (2018). Fuzzy rule learning for material classification from imprecise data. In Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Foundations: 17th International Conference, IPMU 2018, Cádiz, Spain, June 11-15, 2018, Proceedings, Part I 17 (pp. 62-73). Springer International Publishing.
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Material classification from imprecise chemical composition: probabilistic vs possibilistic approach

Published in 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2018

Recommended citation: Grivet Sébert, A., & Poli, J. P. (2018, July). Material classification from imprecise chemical composition: probabilistic vs possibilistic approach. In 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (pp. 1-8). IEEE.
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SPEED: Secure, PrivatE, and Efficient Deep learning

Published in Machine Learning, 2021

Recommended citation: Grivet Sébert, A., Pinot, R., Zuber, M., Gouy-Pailler, C., & Sirdey, R. (2021). SPEED: secure, PrivatE, and efficient deep learning. Machine Learning, 110(4), 675-694.
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Rank Aggregation by Dissatisfaction Minimisation in the Unavailable Candidate Model

Published in 20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2021), 2021

Recommended citation: Grivet Sébert, A., Maudet, N., Perny, P., & Viappiani, P. (2021, May). Rank Aggregation by Dissatisfaction Minimisation in the Unavailable Candidate Model. In 20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2021) (pp. 1518-1520). ACM.
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A secure federated learning framework using homomorphic encryption and verifiable computing

Published in 2021 Reconciling Data Analytics, Automation, Privacy, and Security: A Big Data Challenge (RDAAPS), 2021

Recommended citation: Madi, A., Stan, O., Mayoue, A., Grivet Sébert, A., Gouy-Pailler, C., & Sirdey, R. (2021, May). A secure federated learning framework using homomorphic encryption and verifiable computing. In 2021 Reconciling Data Analytics, Automation, Privacy, and Security: A Big Data Challenge (RDAAPS) (pp. 1-8). IEEE.
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Preference Aggregation in the Generalised Unavailable Candidate Model

Published in Algorithmic Decision Theory: 7th International Conference (ADT 2021), 2021

Recommended citation: Grivet Sébert, A., Maudet, N., Perny, P., & Viappiani, P. (2021). Preference aggregation in the generalised unavailable candidate model. In Algorithmic Decision Theory: 7th International Conference, ADT 2021, Toulouse, France, November 3–5, 2021, Proceedings 7 (pp. 35-50). Springer International Publishing.
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Combining differential privacy and homomorphic encryption for privacy-preserving collaborative machine learning (PhD thesis)

Published in , 2023

Recommended citation: Grivet Sébert, A. (2023). Combining differential privacy and homomorphic encryption for privacy-preserving collaborative machine learning (Doctoral dissertation, Université Paris-Saclay).
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Combining homomorphic encryption and differential privacy in federated learning

Published in 20th Annual International Conference on Privacy, Security and Trust (PST), 2023

Recommended citation: Grivet Sébert, A., Checri, M., Stan, O., Sirdey, R., & Gouy-Pailler, C. (2023, August). Combining homomorphic encryption and differential privacy in federated learning. In 2023 20th Annual International Conference on Privacy, Security and Trust (PST) (pp. 1-7). IEEE.
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A Probabilistic Design for Practical Homomorphic Majority Voting with Intrinsic Differential Privacy

Published in Proceedings of the 11th Workshop on Encrypted Computing & Applied Homomorphic Cryptography, 2023

Recommended citation: Grivet Sébert, A., Zuber, M., Stan, O., Sirdey, R., & Gouy-Pailler, C. (2023, November). A probabilistic design for practical homomorphic majority voting with intrinsic differential privacy. In Proceedings of the 11th Workshop on Encrypted Computing & Applied Homomorphic Cryptography (pp. 47-58).
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Fuzzy rule learning for material classification from imprecise data @ IPMU

Published: June 15, 2018

20-minute presentation of our paper Fuzzy rule learning for material classification from imprecise data.

Material classification from imprecise chemical composition: probabilistic vs possibilistic approach @ FUZZ-IEEE

Published: July 08, 2018

20-minute presentation of our paper Material classification from imprecise chemical composition: probabilistic vs possibilistic approach.

SPEED: secure, PrivatE, and efficient deep learning @ ECML

Published: September 01, 2021

20-minute presentation of our paper SPEED: secure, PrivatE, and efficient deep learning

Preference Aggregation in the Generalised Unavailable Candidate Model @ ADT

Published: November 01, 2021

20-minute presentation on our paper Preference Aggregation in the Generalised Unavailable Candidate Model.

Machine learning without jeopardizing the data @ PODC

Published: July 25, 2022

20-minute presentation on ‘Machine learning without jeopardizing the data’. Check the slides here.

PhD defense: Combining differential privacy and homomorphic encryption for privacy-preserving collaborative machine learning

Published: June 12, 2023

45-minute presentation followed by a discussion on ‘Combining differential privacy and homomorphic encryption for privacy-preserving collaborative machine learning’​. Check the slides here.

Combining homomorphic encryption and differential privacy in federated learning @ PST

Published: August 01, 2023

20-minute presentation on our paper Combining homomorphic encryption and differential privacy in federated learning.

A Probabilistic Design for Practical Homomorphic Majority Voting with Intrinsic Differential Privacy @ WAHC

Published: November 20, 2023

20-minute presentation on our paper A Probabilistic Design for Practical Homomorphic Majority Voting with Intrinsic Differential Privacy.

Student project supervisor

Student project supervision, CentraleSupélec, 2017

I supervised a team of four undergraduate students on a project proposed as a CEA researcher on explainable material classification using genetic algorithms.

Lecturer

Lecture, Institut national des sciences et techniques nucléaires (INSTN), 2020

I designed and taught a 1h30 course about attacks and defenses on data privacy in deep learning.

Lecturer

Lecture, Télécom SudParis, 2021

I designed and taught a 3h course about attacks and defenses on data privacy in deep learning.