27 citations · 65 across the 12 of their papers we have counts for
14 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…
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…
SplitBFT: Improving Byzantine Fault Tolerance Safety Using Trusted Compartments
Ines Messadi, Markus Horst Becker, Kai Bleeke +3
Byzantine fault-tolerant agreement (BFT) in a partially synchronous system usually requires 3f + 1 nodes to tolerate f faulty replicas. Due to their high throughput and finality pr…
ALDER: Unlocking blockchain performance by multiplexing consensus protocols
Kadir Korkmaz, Joachim Bruneau-Queyreix, Sonia Ben Mokthar +1
Most of today's online services (e.g., social networks, search engines, market places) are centralized, which is recognized as unsatisfactory by a majority of users for various rea…
Enhancing Robustness of On-line Learning Models on Highly Noisy Data
Zilong Zhao, Robert Birke, Rui Han +4
Classification algorithms have been widely adopted to detect anomalies for various systems, e.g., IoT, cloud and face recognition, under the common assumption that the data source…
RAD: On-line Anomaly Detection for Highly Unreliable Data
Zilong Zhao, Robert Birke, Rui Han +4
Classification algorithms have been widely adopted to detect anomalies for various systems, e.g., IoT, cloud and face recognition, under the common assumption that the data source…
X-Search: Revisiting Private Web Search using Intel SGX
Sonia Ben Mokhtar, Antoine Boutet, Pascal Felber +3
The exploitation of user search queries by search engines is at the heart of their economic model. As consequence, offering private Web search functionalities is essential to the u…
CYCLOSA: Decentralizing Private Web Search Through SGX-Based Browser Extensions
Rafael Pires, David Goltzsche, Sonia Ben Mokhtar +6
By regularly querying Web search engines, users (unconsciously) disclose large amounts of their personal data as part of their search queries, among which some might reveal sensiti…
Time Distortion Anonymization for the Publication of Mobility Data with High Utility
Vincent Primault, Sonia Ben Mokhtar, Cédric Lauradoux +1
An increasing amount of mobility data is being collected every day by different means, such as mobile applications or crowd-sensing campaigns. This data is sometimes published afte…