Assistant Professor
pejo (at) crysys.hu
web: www.crysys.hu/~pejo/
office: I.E. 430
tel: +36 1 463 2080
Balázs Pejó was born in 1989 in Budapest, Hungary. He received a B.Sc. degree in Mathematics from the Budapest University of Technology and Economics (BME, Hungary) in 2012 and two M.Sc. degree in Computer Science in the Security and Privacy program of EIT Digital from the University of Trento (UNITN, Italy) and Eötvös Loránd University (ELTE, Hungary) in 2014. He earned the Ph.D. degree in Informatics from the University of Luxembourg (UNILU, Luxembourg) in 2019. Currently, he is a member of the Laboratory of Cryptography and Systems Security (CrySyS Lab).
The sharing and explotation of the ever-growing data about individuals raise serious privacy concerns these
days. Is it possible to derive (socially or individually) useful information about people from this Big Data
without revealing personal information?
This course provides a detailed overview of data privacy. It focuses on different privacy problems of web
tracking, data sharing, and machine learning, as well as their mitigation techniques. The aim is to give the
essential (technical) background knowledge needed to identify and protect personal data. These skills are
becoming a must of every data/software engineer and data protection officer dealing with personal and sensitive
data, and are also required by the European General Data Protection Regulation (GDPR).
Machine Learning (Artificial Intelligence) has become undisputedly popular in recent years. The number of security critical applications
of machine learning has been steadily increasing over the years (self-driving cars, user authentication, decision support, profiling, risk assessment, etc.).
However, there are still many open privacy and security problems of machine learning. Students can work on the following topics:
Required skills: none
Preferred skills: basic programming skills (e.g., python), machine learning (not required)
As evidenced in the last 10-15 years, cybersecurity is not a purely technical discipline. Decision-makers, whether sitting at security providers (IT companies), security demanders (everyone using IT) or the security industry, are mostly driven by economic incentives. Understanding these incentives are vital for designing systems that are secure in real-life scenarios. Parallel to this, data privacy has also shown the same characteristics: proper economic incentives and controls are needed to design systems where sharing data is beneficial to both data subject and data controller. An extreme example to a flawed attempt at such a design is the Cambridge Analytica case.
The prospective student will identify a cybersecurity or data privacy economics problem, and use elements of game theory and other domain-specific techniques and software tools to transform the problem into a model and propose a solution. Potential topics include:
Required skills: model thinking, good command of English
Preferred skills: basic knowledge of game theory, basic programming skills (e.g., python, matlab, NetLogo)
Transactions on Data Privacy (TDP), vol. 15, 2022.
Bibtex | Abstract | PDF | Link
@article {
author = {Balazs Pejo, Mina Remeli, Ádám Arany, Mathieu Galtier, Gergely Ács},
title = {Collaborative Drug Discovery: Inference-level Privacy Perspective},
journal = {Transactions on Data Privacy (TDP)},
volume = {15},
year = {2022},
howpublished = "\url{http://www.tdp.cat/issues21/abs.a449a21.php}"
}
ACM Transactions on Spatial Algorithms and Systems (TSAS), 2022.
@article {
author = {Balazs Pejo, Gergely Biczók},
title = {Games in the Time of COVID-19: Promoting Mechanism Design for Pandemic Response},
journal = {ACM Transactions on Spatial Algorithms and Systems (TSAS)},
year = {2022},
howpublished = "\url{https://dl.acm.org/doi/abs/10.1145/3503155}"
}
Springer International Publishing (SpringerBriefs), 2022.
@book {
author = {Balazs Pejo, Damien Desfontaines},
title = {Guide to Differential Privacy Modifications},
publisher = {Springer International Publishing (SpringerBriefs)},
year = {2022},
howpublished = "\url{https://link.springer.com/book/10.1007/978-3-030-96398-9}"
}
Enabling Technologies for Social Distancing: Fundamentals, concepts and solutions, (IET), 2022.
@inproceedings {
author = {Balazs Pejo, Gergely Biczók},
title = {Incentives for Individual Compliance with Pandemic Response Measures},
booktitle = {Enabling Technologies for Social Distancing: Fundamentals, concepts and solutions, (IET)},
year = {2022},
howpublished = "\url{https://digital-library.theiet.org/content/books/te/pbte104e}"
}
AdKDD Workshop at 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (AdKDD) , 2022.
Bibtex | Abstract | PDF | Link
@inproceedings {
author = {Frederick Ayala-Gómez, Ismo Horppu, Erlin Gülbenkoglu, Vesa Siivola, Balazs Pejo},
title = {Revenue Attribution on iOS 14 using Conversion Values in F2P Games},
booktitle = {AdKDD Workshop at 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (AdKDD) },
year = {2022},
howpublished = "\url{https://www.adkdd.org/Papers/Show-me-the-Money%3A-Measuring-Marketing-Performance-in-F2P-Games-using-Apple's-App-Tracking-Transparency-Framework/2022}"
}
22nd Financial Cryptography and Data Security Conference (FC), 2022.
@conference {
author = {Andras Instvan Seres, Balazs Pejo, Peter Burcsi},
title = {Why Fuzzy Message Detection Leads to Fuzzy Privacy Guarantees},
booktitle = {22nd Financial Cryptography and Data Security Conference (FC)},
year = {2022},
howpublished = "\url{https://fc22.ifca.ai/preproceedings/9.pdf}"
}
ERCIM NEWS, vol. 126, 2021, pp. 35-36.
@article {
author = {Gergely Ács, Gergely Biczók, Balazs Pejo},
title = {Measuring Contributions in Privacy-Preserving Federated Learning},
journal = {ERCIM NEWS},
volume = {126},
year = {2021},
pages = {35-36},
howpublished = "\url{https://ercim-news.ercim.eu/en126/special/measuring-contributions-in-privacy-preserving-federated-learning}"
}
18th International Conference on Security and Cryptography (SECRYPT), 2021.
@conference {
author = {Mathias Parisot, Balazs Pejo, Dayana Spagnuelo},
title = {Property Inference Attacks on Convolutional Neural Networks: Influence and Implications of Target Model's Complexity},
booktitle = {18th International Conference on Security and Cryptography (SECRYPT)},
year = {2021},
howpublished = "\url{https://www.scitepress.org/Link.aspx?doi=10.5220/0010555607150721}"
}
Proc. of ACM SIGSPATIAL Workshop on COVID, ACM, 2020.
@inproceedings {
author = {Balazs Pejo, Gergely Biczók},
title = {Corona Games: Masks, Social Distancing and Mechanism Design},
booktitle = {Proc. of ACM SIGSPATIAL Workshop on COVID},
publisher = {ACM},
year = {2020}
}
Proceedings on Privacy Enhancing Technologies (PETS 2019), De Gruyter, 2019.
@inproceedings {
author = {Balazs Pejo, , Gergely Biczók},
title = {Together or Alone: The Price of Privacy in Collaborative Learning},
booktitle = {Proceedings on Privacy Enhancing Technologies (PETS 2019)},
publisher = {De Gruyter},
year = {2019}
}
CCS 2018 Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security, ACM, 2018.
@inproceedings {
author = {Balazs Pejo, , Gergely Biczók},
title = {POSTER: The Price of Privacy in Collaborative Learning},
booktitle = {CCS 2018 Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security},
publisher = {ACM},
year = {2018}
}