CV

Education

• Ph.D in Version Control Theory, GitHub University, 2018 (expected)
• M.S. in Jekyll, GitHub University, 2014
• B.S. in GitHub, GitHub University, 2012

Work experience

• Spring 2024: Academic Pages Collaborator
  • Github University
  • Duties includes: Updates and improvements to template
  • Supervisor: The Users
• Fall 2015: Research Assistant
  • Github University
  • Duties included: Merging pull requests
  • Supervisor: Professor Hub
• Summer 2015: Research Assistant
  • Github University
  • Duties included: Tagging issues
  • Supervisor: Professor Git

Skills

• Skill 1
• Skill 2
  • Sub-skill 2.1
  • Sub-skill 2.2
  • Sub-skill 2.3
• Skill 3

Publications

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

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).

Combining homomorphic encryption and differential privacy in federated learning

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.

Combining differential privacy and homomorphic encryption for privacy-preserving collaborative machine learning (PhD thesis)

Preference Aggregation in the Generalised Unavailable Candidate Model

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.

A secure federated learning framework using homomorphic encryption and verifiable computing

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.

Rank Aggregation by Dissatisfaction Minimisation in the Unavailable Candidate Model

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.

SPEED: Secure, PrivatE, and Efficient Deep learning

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.

Material classification from imprecise chemical composition: probabilistic vs possibilistic approach

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.

Fuzzy rule learning for material classification from imprecise data

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.

Talks

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

November 20, 2023

Talk at Workshop on Encrypted Computing & Applied Homomorphic Cryptography (WAHC), Copenhagen, Denmark

Combining homomorphic encryption and differential privacy in federated learning @ PST

August 01, 2023

Tutorial at Annual Conference on Privacy, Security and Trust (PST), 2023, Copenhagen, Denmark

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

June 12, 2023

Talk at CEA Saclay, Gif-sur-Yvette, France

Machine learning without jeopardizing the data @ PODC

July 25, 2022

Conference proceedings talk at Principles of Distributed Learning (PODL), workshop at 2022 Symposium on Principles of Distributed Computing (PODC), Salerno, Italy

Preference Aggregation in the Generalised Unavailable Candidate Model @ ADT

November 01, 2021

Talk at Algorithmic Decision Theory: 7th International Conference (ADT), Toulouse, France

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

September 01, 2021

Conference proceedings talk at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2021), online

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

July 08, 2018

Conference proceedings talk at 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Rio de Janeiro, Brazil

Fuzzy rule learning for material classification from imprecise data @ IPMU

June 15, 2018

Conference proceedings talk at Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Foundations: 17th International Conference, IPMU 2018, Cádiz, Spain

Teaching

Lecturer

Lecturer

Student project supervisor

Service and leadership

• Currently signed in to 43 different slack teams