13 citations · 53 across the 13 of their papers we have counts for
16 papers
CAN We Trust Your Results? A Cross-Dataset Study of Automotive IDS Evaluation
Beatrix Koltai, Gergely Acs, Andras Gazdag
The increasing connectivity of modern vehicles has made securing in-vehicle communication networks a critical challenge. Intrusion Detection Systems (IDS) have been widely studied…
Interdependent Privacy in Smart Homes: Hunting for Bystanders in Privacy Policies
Shuaishuai Liu, Gergely Acs, Gergely Biczók
Smart home devices such as video doorbells and security cameras are becoming increasingly common in everyday life. While these devices offer convenience and safety, they also raise…
Client-specific Property Inference against Secure Aggregation in Federated Learning
Raouf Kerkouche, Gergely Ács, Mario Fritz
Federated learning has become a widely used paradigm for collaboratively training a common model among different participants with the help of a central server that coordinates the…
Industry-Scale Orchestrated Federated Learning for Drug Discovery
Martijn Oldenhof, Gergely Ács, Balázs Pejó +44
To apply federated learning to drug discovery we developed a novel platform in the context of European Innovative Medicines Initiative (IMI) project MELLODDY (grant n°831472), whic…
Collaborative Drug Discovery: Inference-level Data Protection Perspective
Balazs Pejo, Mina Remeli, Adam Arany +2
Pharmaceutical industry can better leverage its data assets to virtualize drug discovery through a collaborative machine learning platform. On the other hand, there are non-negligi…
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…
In Search of Lost Utility: Private Location Data
Szilvia Lestyán, Gergely Ács, Gergely Biczók
The unavailability of training data is a permanent source of much frustration in research, especially when it is due to privacy concerns. This is particularly true for location dat…
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…
Differentially Private Mixture of Generative Neural Networks
Gergely Acs, Luca Melis, Claude Castelluccia +1
Generative models are used in a wide range of applications building on large amounts of contextually rich information. Due to possible privacy violations of the individuals whose d…
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…
On the Unicity of Smartphone Applications
Jagdish Prasad Achara, Gergely Acs, Claude Castelluccia
Prior works have shown that the list of apps installed by a user reveal a lot about user interests and behavior. These works rely on the semantics of the installed apps and show th…
Retargeting Without Tracking
Minh-Dung Tran, Gergely Acs, Claude Castelluccia
Retargeting ads are increasingly prevalent on the Internet as their effectiveness has been shown to outperform conventional targeted ads. Retargeting ads are not only based on user…
DREAM: DiffeRentially privatE smArt Metering
Gergely Acs, Claude Castelluccia
This paper presents a new privacy-preserving smart metering system. Our scheme is private under the differential privacy model and therefore provides strong and provable guarantees…