Bio

I am a postdoctoral researcher in the DRIM Team at CNRS and INSA Lyon under the supervision of Sonia Ben Mokhtar. I am interested in the study of privacy and security in the context of data-processing techniques. I work with information theoretical notions such as differential privacy as well as more security oriented notions such as proofs by simulation in the presence of malicious adversaries. I am currently studying the conditions in which decentralized or gossip protocols satisfy the aforementioned properties.

I did my PhD in the MAGNET team at INRIA-Lille under the supervision of Jan Ramon. At MAGNET, I studied similar topics as in my current post-doc. In particular, I studied how to improve privacy-accuracy-communication trade offs and robustness in decentralized algorithms via correlated noise and cryptographic primitives such as zero-knowledge proofs and multiparty computation.

Previous to starting a career on privacy, I explored several topics of Computing Science. Some of them are the following: I did research in high performance computing at the INRIA-Strasbourg CAMUS team, followed summer courses of Rob Morris in extremal graph and combinatorics theory at the Institute of Pure and Applied Mathematics (IMPA) in Rio, Brazil and studied formal methods as part of my masters at Universidad Nacional de Rosario, Argentina.

I am from Margarita, a small town in the middle of the countryside in Santa Fe, Argentina. During secondary school, I enjoyed participating in algorithm design contests. I was bronze medalist at the National Olympiad in Informatics and first substitute of the national selection team for the International Olympiad of Informatics (IOI).

I am Erdös number 4 via Jan, my PhD supervisor. You can find my CV here and my Google scholar profile here.

Service

• NeurIPS 2023 (top 8%) Reviewer
• ICML 2023 Reviewer

Publications

• César Sabater, Florian Hahn, Andreas Peter, and Jan Ramon. "Private Sampling with Identifiable Cheaters." Proceedings on Privacy Enhancing Technologies 2023.2 (2023). [ Paper ]


• César Sabater, Aurélien Bellet, and Jan Ramon. "An accurate, scalable and verifiable protocol for federated differentially private averaging." Machine Learning (2022): 1-45. [ Paper | arXiv ]


• César Sabater and Jan Ramon. "Zero Knowledge Arguments for Verifiable Sampling." NeurIPS 2021 Workshop Privacy in Machine Learning. 2021. [ Paper ]


• César Sabater, Aurélien Bellet, and Jan Ramon. "Distributed Differentially Private Averaging with Improved Utility and Robustness to Malicious Parties." NeurIPS 2020 workshop on Privacy Preserving Machine Learning-PriML and PPML Joint Edition. 2020. [ arXiv ]


• Maxime Schmitt, César Sabater, and Cédric Bastoul. "Semi-Automatic Generation of Adaptive Codes." In IMPACT 2017, 7th International Workshop on Polyhedral Compilation Techniques. 2017. [ HAL]


• Cédric Bastoul and César Sabater. "Automatic Generation of Adaptive Simulation Codes." In SimRace, Conference on Numerical Methods and High Performance Computing for Industrial Fluid Flows. 2015. [ HAL ]


• Ana Casali, Claudia Deco, Cristina Bender, Santiago Fontanarrosa, and César Sabater. "Asistente para el eepósito de objetos en repositorios con extracción automática de metadatos." In XV Simposio Internacional de Tecnologías de la Información y las Comunicaciones en la Educación SINTICE, pp. 133-136. 2013. [ Researchgate ]

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