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Pages
Posts
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Blog Post number 4
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Blog Post number 1
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portfolio
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publications
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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talks
Fuzzy rule learning for material classification from imprecise data @ IPMU
Published:
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:
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:
20-minute presentation of our paper SPEED: secure, PrivatE, and efficient deep learning
Preference Aggregation in the Generalised Unavailable Candidate Model @ ADT
Published:
20-minute presentation on our paper Preference Aggregation in the Generalised Unavailable Candidate Model.
Machine learning without jeopardizing the data @ PODC
Published:
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:
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:
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:
20-minute presentation on our paper A Probabilistic Design for Practical Homomorphic Majority Voting with Intrinsic Differential Privacy.
teaching
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.