activity
20152022
most citedEnhancing Robustness of On-line Learning Models on Highly Noisy Data

11 citations · 12 across the 4 of their papers we have counts for

collaborators

7 papers

cs.DC2022

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…

cs.DC2022

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…

cs.LG202111 cited

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…

cs.LG20191 cited

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…

cs.DC2018

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…

cs.DC2018

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…