27 citations · 65 across the 12 of their papers we have counts for
7 papers · 1 filter
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…
On the Normalization of Confusion Matrices: Methods and Geometric Interpretations
Johan Erbani, Pierre-Edouard Portier, Elod Egyed-Zsigmond +2
The confusion matrix is a standard tool for evaluating classifiers by providing insights into class-level errors. In heterogeneous settings, its values are shaped by two main facto…
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…
Dropout-Robust Mechanisms for Differentially Private and Fully Decentralized Mean Estimation
César Sabater, Sonia Ben Mokhtar, Jan Ramon
Achieving differentially private computations in decentralized settings poses significant challenges, particularly regarding accuracy, communication cost, and robustness against in…
A Weighted Loss Approach to Robust Federated Learning under Data Heterogeneity
Johan Erbani, Sonia Ben Mokhtar, Pierre-Edouard Portier +2
Federated learning (FL) is a machine learning paradigm that enables multiple data holders to collaboratively train a machine learning model without sharing their training data with…
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…
Secure Federated Graph-Filtering for Recommender Systems
Julien Nicolas, César Sabater, Mohamed Maouche +2
Recommender systems often rely on graph-based filters, such as normalized item-item adjacency matrices and low-pass filters. While effective, the centralized computation of these c…