3 citations · 7 across the 7 of their papers we have counts for
7 papers
TriHaRd: Higher Resilience for TEE Trusted Time
Matthieu Bettinger, Sonia Ben Mokhtar, Pascal Felber +3
Accurately measuring time passing is critical for many applications. However, in Trusted Execution Environments (TEEs) such as Intel SGX, the time source is outside the Trusted Com…
An Open-source Implementation and Security Analysis of Triad's TEE Trusted Time Protocol
Matthieu Bettinger, Sonia Ben Mokhtar, Anthony Simonet-Boulogne
The logic of many protocols relies on time measurements. However, in Trusted Execution Environments (TEEs) like Intel SGX, the time source is outside the Trusted Computing Base: a…
GRANITE : a Byzantine-Resilient Dynamic Gossip Learning Framework
Yacine Belal, Mohamed Maouche, Sonia Ben Mokhtar
Gossip Learning (GL) is a decentralized learning paradigm where users iteratively exchange and aggregate models with a small set of neighboring peers. Recent approaches rely on dyn…
Reliability is Blind: Collective Incentives for Decentralized Computing Marketplaces without Individual Behavior Information
Henry Mont, Matthieu Bettinger, Sonia Ben Mokhtar +1
In decentralized cloud computing marketplaces, ensuring fair and efficient interactions among asset providers and end-users is crucial. A key concern is meeting agreed-upon service…
COoL-TEE: Client-TEE Collaboration for Resilient Distributed Search
Matthieu Bettinger, Etienne Rivière, Sonia Ben Mokhtar +1
Current marketplaces rely on search mechanisms with distributed systems but centralized governance, making them vulnerable to attacks, failures, censorship and biases. While search…
TEE-based Key-Value Stores: a Survey
Aghiles Ait Messaoud, Sonia Ben Mokhtar, Anthony Simonet-Boulogne
Key-Value Stores (KVSs) are No-SQL databases that store data as key-value pairs and have gained popularity due to their simplicity, scalability, and fast retrieval capabilities. Ho…
Inferring Communities of Interest in Collaborative Learning-based Recommender Systems
Yacine Belal, Sonia Ben Mokhtar, Mohamed Maouche +1
Collaborative-learning-based recommender systems, such as those employing Federated Learning (FL) and Gossip Learning (GL), allow users to train models while keeping their history…