2 citations · 2 across the 1 of their papers we have counts for
6 papers
Constrained Differentially Private Federated Learning for Low-bandwidth Devices
Raouf Kerkouche, Gergely Ács, Claude Castelluccia +1
Federated learning becomes a prominent approach when different entities want to learn collaboratively a common model without sharing their training data. However, Federated learnin…
Compression Boosts Differentially Private Federated Learning
Raouf Kerkouche, Gergely Ács, Claude Castelluccia +1
Federated Learning allows distributed entities to train a common model collaboratively without sharing their own data. Although it prevents data collection and aggregation by excha…
Federated Learning in Adversarial Settings
Raouf Kerkouche, Gergely Ács, Claude Castelluccia
Federated Learning enables entities to collaboratively learn a shared prediction model while keeping their training data locally. It prevents data collection and aggregation and, t…
Automatic Driver Identification from In-Vehicle Network Logs
Mina Remeli, Szilvia Lestyan, Gergely Acs +1
Data generated by cars is growing at an unprecedented scale. As cars gradually become part of the Internet of Things (IoT) ecosystem, several stakeholders discover the value of in-…
Extracting vehicle sensor signals from CAN logs for driver re-identification
Szilvia Lestyan, Gergely Acs, Gergely Biczok +1
Data is the new oil for the car industry. Cars generate data about how they are used and who's behind the wheel which gives rise to a novel way of profiling individuals. Several pr…
Near-Optimal Fingerprinting with Constraints
Gabor Gyorgy Gulyas, Gergely Acs, Claude Castelluccia
Several recent studies have demonstrated that people show large behavioural uniqueness. This has serious privacy implications as most individuals become increasingly re-identifiabl…