Provable Security of On-Demand Distance Vector Routing in Wireless Ad Hoc Networks. - Gergely Ács; Levente Buttyán; István Vajda (ESAS, 2005)

Modelling adversaries and security objectives for routing protocols in wireless sensor networks. - Gergely Ács; Levente Buttyán; István Vajda (SASN, 2006)

Provably Secure On-Demand Source Routing in Mobile Ad Hoc Networks. - Gergely Ács; Levente Buttyán; István Vajda (IEEE Trans. Mob. Comput., 2006)

The Security Proof of a Link-state Routing Protocol for Wireless Sensor Networks. - Gergely Ács; Levente Buttyán; István Vajda (MASS, 2007)

Biztonságos útvonalválasztás többugrásos vezetéknélküli hálózatokban. - Gergely Ács (2009)

Misbehaving router detection in link-state routing for wireless mesh networks. - Gergely Ács; Levente Buttyán; László Dóra (WOWMOM, 2010)

I Have a DREAM! (DiffeRentially privatE smArt Metering). - Gergely Ács; Claude Castelluccia (Information Hiding, 2011)

Protecting against physical resource monitoring. - Gergely Ács; Claude Castelluccia; William Lecat (WPES, 2011)

Differentially private sequential data publication via variable-length n-grams. - Rui Chen 012; Gergely Ács; Claude Castelluccia (CCS, 2012)

Differentially Private Histogram Publishing through Lossy Compression. - Gergely Ács; Claude Castelluccia; Rui Chen 012 (ICDM, 2012)

You are what you like! Information leakage through users' Interests. - Chaabane Abdelberi; Gergely Ács; Mohamed Ali Kâafar (NDSS, 2012)

DREAM: DiffeRentially privatE smArt Metering - Gergely Ács; Claude Castelluccia (CoRR, 2012)

Cache Privacy in Named-Data Networking. - Gergely Ács; Mauro Conti; Paolo Gasti; Cesar Ghali; Gene Tsudik (ICDCS, 2013)

A case study: privacy preserving release of spatio-temporal density in paris. - Gergely Ács; Claude Castelluccia (KDD, 2014)

Retargeting Without Tracking. - Minh-Dung Tran; Gergely Ács; Claude Castelluccia (CoRR, 2014)

Probabilistic km-anonymity efficient anonymization of large set-valued datasets. - Gergely Ács; Jagdish Prasad Achara; Claude Castelluccia (IEEE BigData, 2015)

On the Unicity of Smartphone Applications. - Jagdish Prasad Achara; Gergely Ács; Claude Castelluccia (WPES@CCS, 2015)

Near-Optimal Fingerprinting with Constraints. - Gábor György Gulyás; Gergely Ács; Claude Castelluccia (Proc. Priv. Enhancing Technol., 2016)

Privacy-Preserving Release of Spatio-Temporal Density. - Gergely Ács; Gergely Biczók; Claude Castelluccia (Handbook of Mobile Data Privacy, 2018)

Extracting Vehicle Sensor Signals from CAN Logs for Driver Re-identification. - Szilvia Lestyan; Gergely Ács; Gergely Biczók; Zsolt Szalay (ICISSP, 2019)

Automatic Driver Identification from In-Vehicle Network Logs. - Mina Remeli; Szilvia Lestyán; Gergely Ács; Gergely Biczók (ITSC, 2019)

Differential Inference Testing: A Practical Approach to Evaluate Sanitizations of Datasets. - Ali Kassem 001; Gergely Ács; Claude Castelluccia; Catuscia Palamidessi (IEEE Symposium on Security and Privacy Workshops, 2019)

Privacy-Aware Caching in Information-Centric Networking. - Gergely Ács; Mauro Conti; Paolo Gasti; Cesar Ghali; Gene Tsudik; Christopher A. Wood (IEEE Trans. Dependable Secur. Comput., 2019)

Differentially Private Mixture of Generative Neural Networks. - Gergely Ács; Luca Melis; Claude Castelluccia; Emiliano De Cristofaro (IEEE Trans. Knowl. Data Eng., 2019)

Privacy-preserving release of mobility data: a clean-slate approach. - Szilvia Lestyán; Gergely Ács; Gergely Biczók (CoRR, 2020)

Federated Learning in Adversarial Settings. - Raouf Kerkouche; Gergely Ács; Claude Castelluccia (CoRR, 2020)

Privacy-preserving and bandwidth-efficient federated learning: an application to in-hospital mortality prediction. - Raouf Kerkouche; Gergely Ács; Claude Castelluccia; Pierre Genevès (CHIL, 2021)

Compression Boosts Differentially Private Federated Learning. - Raouf Kerkouche; Gergely Ács; Claude Castelluccia; Pierre Genevès (EuroS&P, 2021)

Constrained differentially private federated learning for low-bandwidth devices. - Raouf Kerkouche; Gergely Ács; Claude Castelluccia; Pierre Genevès (UAI, 2021)

Measuring Contributions in Privacy-Preserving Federated Learning. - Balázs Pejó; Gergely Biczók; Gergely Ács (ERCIM News, 2021)

SIMBIoTA-ML: Light-weight, Machine Learning-based Malware Detection for Embedded IoT Devices. - Dorottya Papp; Gergely Ács; Roland Nagy; Levente Buttyán (IoTBDS, 2022)

Industry-Scale Orchestrated Federated Learning for Drug Discovery. - Martijn Oldenhof; Gergely Ács; Balázs Pejó; Ansgar Schuffenhauer; Nicholas Holway; Noé Sturm; Arne Dieckmann; Oliver Fortmeier; Eric Boniface; Clément Mayer; Arnaud Gohier; Peter Schmidtke; et al. (CoRR, 2022)

In Search of Lost Utility: Private Location Data. - Szilvia Lestyán; Gergely Ács; Gergely Biczók (Proc. Priv. Enhancing Technol., 2022)

Collaborative Drug Discovery: Inference-level Data Protection Perspective. - Balazs Pejo; Mina Remeli; Adam Arany; Mathieu Galtier; Gergely Ács (Trans. Data Priv., 2022)

Industry-Scale Orchestrated Federated Learning for Drug Discovery. - Martijn Oldenhof; Gergely Ács; Balázs Pejó; Ansgar Schuffenhauer; Nicholas Holway; Noé Sturm; Arne Dieckmann; Oliver Fortmeier; Eric Boniface; Clément Mayer; Arnaud Gohier; Peter Schmidtke; et al. (AAAI, 2023)

Client-specific Property Inference against Secure Aggregation in Federated Learning. - Raouf Kerkouche; Gergely Ács; Mario Fritz (WPES@CCS, 2023)

Privacy pitfalls of releasing in-vehicle network data. - András Gazdag; Szilvia Lestyán; Mina Remeli; Gergely Ács; Tamás Holczer; Gergely Biczók (Veh. Commun., 2023)

SECURED for Health: Scaling Up Privacy to Enable the Integration of the European Health Data Space. - Francesco Regazzoni 001; Gergely Ács; Albert Zoltan Aszalos; Christos Avgerinos; Nikolaos Bakalos; Josep Lluis Berral; Joppe W. Bos; Marco Brohet; Andrés G. Castillo Sanz; Gareth T. Davies; Stefanos Florescu; Pierre-Elisée Flory; et al. (DATE, 2024)

Supporting CAN Bus Anomaly Detection with Correlation Data. - Beatrix Koltai; András Gazdag; Gergely Ács (ICISSP, 2024)

MELLODDY: Cross-pharma Federated Learning at Unprecedented Scale Unlocks Benefits in QSAR without Compromising Proprietary Information. - Wouter Heyndrickx; Lewis H. Mervin; Tobias Morawietz; Noé Sturm; Lukas Friedrich; Adam Zalewski; Anastasia Pentina; Lina Humbeck; Martijn Oldenhof; Ritsuya Niwayama; Peter Schmidtke; Nikolas Fechner; et al. (J. Chem. Inf. Model., 2024)

Anomaly Detection in Telemonitoring Using Sensor Correlation. - Beatrix Koltai; Gergely Ács; András Gazdag (ERCIM News, 2025)