The CEDRIC seminar, will host a talk by Sara Tucci, Head of the Laboratory at CEA List, France. Her talk will be followed by presentations from PhD students of the CEDRIC Laboratory, a brainstorming session, and a poster session.
When: March 7, 2025 at 2 pm to 6:30 pm.
Where: room 33.1.27C, 2 rue Conté, CNAM, Paris, France.
Speaker Sara Tucci, CEA List
Title: Understanding the Ethereum’s Proof-of-Stake Protocol
Abstract: Ethereum’s recent upgrade, known as the Merge, transitioned the blockchain to a Proof-of-Stake model, introducing an original consensus protocol that combines elements of both Nakamoto-style and Byzantine Fault Tolerance designs. This shift led to a complex protocol that, at the time of implementation, was only partially documented. In this talk, I will present our analysis of Ethereum’s Proof-of-Stake protocol, examining its safety, liveness, and incentive compatibility across various network models. Our findings reveal that, in an eventually synchronous network without participant churn, Ethereum’s Proof-of-Stake protocol ensures safety but achieves only probabilistic liveness. Moreover, with the inclusion of the Inactivity Leak mechanism—which removes inactive validators—we identified potential safety vulnerabilities. Finally, we demonstrate that the protocol ensures incentive compatibility in a synchronous setting and achieves eventual incentive compatibility in an eventually synchronous environment.
Speaker: Billel Hakem
Title: Synthetic data generation for predictive maintenance
Bio: I am an engineer from ECE Paris, where I studied computer science and data analysis. I worked as an engineer for Gaussin and Stellantis as a python developer for a year before joining a CIFRE PhD program. The PhD program is a collaboration between le CNAM , ECE Paris and KNDS France. The Thesis is directed by Samia BOUZEFRANE (CNAM) , and supervised by Jae Yun JUN KIM (ECE) and Philippe MILLET (KNDS).
Abstract: Predictive maintenance aim at detecting anomalies and predictive the remaining useful life of industrial systems using sensor data and deep learning algorithms. Due to concern of data privacy and data scarcity, there’s a need to explore alternate alternative to acquire enough data for predictive maintenance systems. We propose a physical simulation for data generation and a test on a diagnosis and prognosis task.