Research assistant
lestyan (at) crysys.hu
web: www.crysys.hu/~lestyan/
office: I.E. 429
tel: +36 1 463 2063
Szilvia obtained both her MSc and BSc diplomas in Applied Mathematics at the Eötvös Loránd University (Hungary), where she specialized in computer science. She has been doing research in the CrySyS Lab. under the guidance of Dr Gergely Ács and Dr. Gergely Biczók since 2017.
This laboratory extends and deepens the knowledge and skills obtained in the courses of the IT Security minor specialization by solving practical, hands-on exercises in real, or close-to-real environments.
Adversarial examples are inputs to machine learning models that an attacker has intentionally designed to cause the model to make a mistake. Such an example is when the input image clearly pictures a school bus, but the model identifies it as an ostrich. This course provides a detailed overview of the security of machine learning systems. It focuses on attack and defense techniques and the theoretical background mainly of adversarial examples.
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)
Privacy Enhancing Technologies Symposium (PETS), 2022.
Bibtex | Abstract | PDF | Link
@conference {
author = {Szilvia Lestyan, Gergely Ács, Gergely Biczók},
title = {In search of lost utility: private location data},
booktitle = {Privacy Enhancing Technologies Symposium (PETS)},
year = {2022},
howpublished = "\url{https://arxiv.org/pdf/2008.01665.pdf}"
}
Jajodia S., Samarati P., Yung M. (eds) Encyclopedia of Cryptography, Security and Privacy. Springer, Berlin, Heidelberg., Springer, 2021.
@inproceedings {
author = {Gergely Ács, Szilvia Lestyan, Gergely Biczók},
title = {Privacy of Aggregated Mobility Data},
booktitle = {Jajodia S., Samarati P., Yung M. (eds) Encyclopedia of Cryptography, Security and Privacy. Springer, Berlin, Heidelberg.},
publisher = {Springer},
year = {2021},
howpublished = "\url{https://doi.org/10.1007/978-3-642-27739-9_1575-1}"
}
22th IEEE Intelligent Transportation Systems Conference (ITSC), IEEE, 2019.
@inproceedings {
author = {Mina Remeli, Szilvia Lestyan, Gergely Ács, Gergely Biczók},
title = {Automatic Driver Identification from In-Vehicle Network Logs},
booktitle = {22th IEEE Intelligent Transportation Systems Conference (ITSC)},
publisher = {IEEE},
year = {2019},
howpublished = "\url{https://arxiv.org/pdf/1911.09508.pdf}"
}
5th International Conference on Information Security and Privacy (ICISSP 2019), SCITEPRESS, 2019, shortlisted for Best Student Paper Award.
@inproceedings {
author = {Szilvia Lestyan, Gergely Ács, Gergely Biczók, Zsolt Szalay},
title = {Extracting vehicle sensor signals from CAN logs for driver re-identification},
booktitle = {5th International Conference on Information Security and Privacy (ICISSP 2019)},
publisher = {SCITEPRESS},
year = {2019},
note = {shortlisted for Best Student Paper Award}
}
Infocommunications Journal, pp. 7-15, December 2016, Volume VIII, Number 4, ISSN 2061-2079, 2016.
@article {
author = {Szilvia Lestyan},
title = {Privacy Preserving Data Aggregation over Multi-hop Networks},
journal = {Infocommunications Journal, pp. 7-15, December 2016, Volume VIII, Number 4, ISSN 2061-2079},
year = {2016}
}
Cognitive Infocommunications (CogInfoCom), 2014 5th IEEE Conference on Cognitive Infocommunications, 2014.
@article {
author = {Szilvia Lestyan, Adrián Csiszárik, András Lukács},
title = {Efficient Apriori Based Algorithms for Privacy Preserving Frequent Itemset Mining},
journal = {Cognitive Infocommunications (CogInfoCom), 2014 5th IEEE Conference on Cognitive Infocommunications},
year = {2014}
}