Game-Theoretic Framework for Integrity Veri cation in Computation Outsourcing. - Qiang Tang 001; Balazs Pejo (IACR Cryptol. ePrint Arch., 2016)

Protect both Integrity and Confidentiality in Outsourcing Collaborative Filtering Computations. - Qiang Tang 001; Balazs Pejo; Husen Wang (IACR Cryptol. ePrint Arch., 2016)

To Cheat or Not to Cheat: A Game-Theoretic Analysis of Outsourced Computation Verification. - Balazs Pejo; Qiang Tang 001 (SCC@AsiaCCS, 2017)

Together or Alone: The Price of Privacy in Joint Learning. - Balazs Pejo; Qiang Tang 001; Afonso Arriaga (CoRR, 2017)

The Price of Privacy in Collaborative Learning. - Balázs Pejó; Qiang Tang 001; Gergely Biczók (CCS, 2018)

Together or Alone: The Price of Privacy in Collaborative Learning. - Balazs Pejo; Qiang Tang 001; Gergely Biczók (Proc. Priv. Enhancing Technol., 2019)

Corona Games: Masks, Social Distancing and Mechanism Design. - Balázs Pejó; Gergely Biczók (COVID@SIGSPATIAL, 2020)

Corona Games: Masks, Social Distance and Mechanism Design. - Balazs Pejo; Gergely Biczók (CoRR, 2020)

The Good, The Bad, and The Ugly: Quality Inference in Federated Learning. - Balázs Pejó (CoRR, 2020)

SoK: Differential privacies. - Damien Desfontaines; Balázs Pejó (Proc. Priv. Enhancing Technol., 2020)

Property Inference Attacks on Convolutional Neural Networks: Influence and Implications of Target Model's Complexity. - Mathias P. M. Parisot; Balázs Pejó; Dayana Spagnuelo (SECRYPT, 2021)

Revenue Attribution on iOS 14 using Conversion Values in F2P Games. - Frederick Ayala-Gómez; Ismo Horppu; Erlin Gulbenkoglu; Vesa Siivola; Balazs Pejo (CoRR, 2021)

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

The Effect of False Positives: Why Fuzzy Message Detection Leads to Fuzzy Privacy Guarantees? - István András Seres; Balázs Pejó; Péter Burcsi (IACR Cryptol. ePrint Arch., 2021)

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)

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

Games in the Time of COVID-19: Promoting Mechanism Design for Pandemic Response. - Balázs Pejó; Gergely Biczók (ACM Trans. Spatial Algorithms Syst., 2022)

Guide to Differential Privacy Modifications - A Taxonomy of Variants and Extensions - Balázs Pejó; Damien Desfontaines (Springer, 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)

SQLi Detection with ML: A Data-Source Perspective. - Balázs Pejó; Nikolett Kapui (SECRYPT, 2023)

Quality Inference in Federated Learning With Secure Aggregation. - Balázs Pejó; Gergely Biczók (IEEE Trans. Big Data, 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)

Effective Anonymous Messaging: The Role of Altruism. - Marcell Frank; Balázs Pejó; Gergely Biczók (GameSec, 2024)

FRIDA: Free-Rider Detection using Privacy Attacks. - Pol G. Recasens; Ádám Horváth; Alberto Gutierrez-Torre; Jordi Torres; Josep Lluis Berral; Balázs Pejó (CoRR, 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)

DeVoS: Deniable Yet Verifiable Vote Updating. - Johannes Müller 001; Balázs Pejó; Ivan Pryvalov (Proc. Priv. Enhancing Technol., 2024)

Inferring Contributions in Privacy-Preserving Federated Learning. - Balázs Pejó; Delio Jaramillo-Velez (ERCIM News, 2025)

Detect & Score: Privacy-Preserving Misbehaviour Detection and Contribution Evaluation in Federated Learning. - Marvin Xhemrishi; Alexandre Graell i Amat; Balázs Pejó (CoRR, 2025)