Dr. Balázs Pejó

Postdoctoral Researcher

pejo (at) crysys.hu

web: www.crysys.hu/~pejo/
office: I.E. 430
tel: +36 1 463 2080
fax: +36 1 463 3263

Current courses | Student projects | Publications

Short Bio

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).

Current Courses

Security and Privacy: an Economic Approach (in English) (VIHIAV34)

Information security is as much an economic problem as it is technical. Even given flawless cryptographic protocols and the availability of perfectly secure software, the misaligned economic incentives of different stakeholders in a system often result in a (very) sub-optimal security level. By guiding you through the jungle of asymmetric information, interdependent security, correlated risk and other concepts characteristic for system security, this elective course will enable you to make better decisions in risk management, security investment and policy design on a system level. Furthermore, the course touches upon the economic aspects of data privacy, an emerging area of interest for users and companies in the big data era.

Privacy-Preserving Technologies (VIHIAV35)

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).

Student Project Proposals

Security and Privacy in/with Machine Learning

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)

Economics of cybersecurity and data privacy

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)

Publications

2021

Measuring Contributions in Privacy-Preserving Federated Learning

G. Ács, G. Biczók, B. Pejo

ERCIM NEWS, vol. 126, 2021, pp. 35-36.

Bibtex | Abstract | Link

@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}"
}

Abstract

How vital is each participant’s contribution to a collaboratively trained machine learning model? This is a challenging question to answer, especially if the learning is carried out in a privacy-preserving manner with the aim of concealing individual actions.

2020

Corona Games: Masks, Social Distancing and Mechanism Design

B. Pejo, G. Biczók

Proc. of ACM SIGSPATIAL Workshop on COVID, ACM, 2020.

Bibtex | Abstract | PDF

@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}
}

Abstract

Pandemic response is a complex affair. Most governments employ a set of quasi-standard measures to fight COVID-19 including wearing masks, social distancing, virus testing and contact tracing. We argue that some non-trivial factors behind the varying effectiveness of these measures are selfish decision-making and the differing national implementations of the response mechanism. In this paper, through simple games, we show the effect of individual incentives on the decisions made with respect to wearing masks and social distancing, and how these may result in a sub-optimal outcome. We also demonstrate the responsibility of national authorities in designing these games properly regarding the chosen policies and their influence on the preferred outcome. We promote a mechanism design approach: it is in the best interest of every government to carefully balance social good and response costs when implementing their respective pandemic response mechanism.