@{MTMT:32546390,
	title = {Digitális lábnyomok és hatásuk a pénzügyi rendszerre},
	url = {https://m2.mtmt.hu/api/publication/32546390},
	author = {Gulyás, Gábor György and Gönczy, László and Kocsis, Imre and Magyar, Gábor and Papp, Dávid and Szűcs, Gábor},
	booktitle = {A digitális transzformáció technológiai kérdései},
	unique-id = {32546390},
	year = {2021},
	pages = {19-171},
	orcid-numbers = {Szűcs, Gábor/0000-0002-5781-1088}
}

@article{MTMT:32532422,
	title = {A Comparative Study on the Privacy Risks of Face Recognition Libraries},
	url = {https://m2.mtmt.hu/api/publication/32532422},
	author = {Fábián, István and Gulyás, Gábor György},
	doi = {10.14232/actacyb.289662},
	journal-iso = {ACTA CYBERN-SZEGED},
	journal = {ACTA CYBERNETICA},
	volume = {25},
	unique-id = {32532422},
	issn = {0324-721X},
	year = {2021},
	eissn = {2676-993X},
	pages = {233-255}
}

@book{MTMT:32038979,
	title = {Risk Mitigation of Facial Recognition},
	url = {https://m2.mtmt.hu/api/publication/32038979},
	author = {Fábián, István and Gulyás, Gábor György},
	publisher = {BME Department of Automation and Applied Informatics},
	unique-id = {32038979},
	year = {2021}
}

@inproceedings{MTMT:31721503,
	title = {Analysis of Demographic Attributes of Face Imprints},
	url = {https://m2.mtmt.hu/api/publication/31721503},
	author = {Fábián, István and Gulyás, Gábor György and Kiséry, Máté Soma},
	booktitle = {Proceedings of the Automation and Applied Computer Science Workshop 2020},
	unique-id = {31721503},
	year = {2020},
	pages = {136-145}
}

@article{MTMT:31615403,
	title = {De-anonymizing Facial Recognition Embeddings},
	url = {https://m2.mtmt.hu/api/publication/31615403},
	author = {Fábián, István and Gulyás, Gábor György},
	doi = {10.36244/ICJ.2020.2.7},
	journal-iso = {INFOCOMM J},
	journal = {INFOCOMMUNICATIONS JOURNAL},
	volume = {12},
	unique-id = {31615403},
	issn = {2061-2079},
	abstract = {Advances of machine learning and hardware getting cheaper resulted in smart cameras equipped with facial recognition becoming unprecedentedly widespread worldwide. Undeniably, this has a great potential for a wide spectrum of uses, it also bears novel risks. In our work, we consider a specific related risk, one related to face embeddings, which are machine learning created metric values describing the face of a person. While embeddings seems arbitrary numbers to the naked eye and are hard to interpret for humans, we argue that some basic demographic attributes can be estimated from them and these values can be then used to look up the original person on social networking sites. We propose an approach for creating synthetic, life-like datasets consisting of embeddings and demographic data of several people. We show over these ground truth datasets that the aforementioned re-identifications attacks do not require expert skills in machine learning in order to be executed. In our experiments, we find that even with simple machine learning models the proportion of successfully re-identified people vary between 6.04% and 28.90%, depending on the population size of the simulation.},
	year = {2020},
	eissn = {2061-2125},
	pages = {50-56}
}

@book{MTMT:31522823,
	title = {Proceedings of the Automation and Applied Computer Science Workshop 2020. AACS’20},
	url = {https://m2.mtmt.hu/api/publication/31522823},
	isbn = {9789634218166},
	editor = {Dunaev, Dmitriy and Gulyás, Gábor György and Vajk, István},
	publisher = {BME Villamosmérnöki és Informatikai Kar},
	unique-id = {31522823},
	year = {2020},
	orcid-numbers = {Vajk, István/0000-0002-2818-9162}
}

@CONFERENCE{MTMT:31331712,
	title = {On the Privacy Risks of Large-Scale Processing of Face Imprints},
	url = {https://m2.mtmt.hu/api/publication/31331712},
	author = {Fábián, István and Gulyás, Gábor György},
	booktitle = {The 12th Conference of PhD Students in Computer Science},
	unique-id = {31331712},
	abstract = {As technology advances, the number of applications relying on face recognition is on the rise. While facial recognition technologies have many benefits, it's important to use them in a responsible manner in order to avoid privacy risks. In this paper we analyze the privacy risks of the processing of face imprints generated by face recognition technologies. We characterize the risks of re-identification attacks against facial imprint databases in multiple scenarios regarding different attacker strength. Our findings show that even if a large number of subjects are surveilled and the attacker can only inefficiently benefit from using the embeddings, the risk of re-identification is still concerning.},
	keywords = {IDENTIFICATION; FACIAL RECOGNITION; Privacy; risk; face embedding; deep metrics},
	year = {2020},
	pages = {92-95}
}

@article{MTMT:3334200,
	title = {Hiding information against structural re-identification},
	url = {https://m2.mtmt.hu/api/publication/3334200},
	author = {Gulyás, Gábor György and Imre, Sándor},
	doi = {10.1007/s10207-018-0400-x},
	journal-iso = {INT J INF SECUR},
	journal = {INTERNATIONAL JOURNAL OF INFORMATION SECURITY},
	volume = {18},
	unique-id = {3334200},
	issn = {1615-5262},
	year = {2019},
	eissn = {1615-5270},
	pages = {125-132},
	orcid-numbers = {Imre, Sándor/0000-0002-2883-8919}
}

@inproceedings{MTMT:30404719,
	title = {To Extend or not to Extend: On the Uniqueness of Browser Extensions and Web Logins},
	url = {https://m2.mtmt.hu/api/publication/30404719},
	author = {Gulyás, Gábor György and Some, Doliere Francis and Bielova, Nataliia and Castelluccia, Claude},
	booktitle = {Proceedings of the 2018 Workshop on Privacy in the Electronic Society - WPES'18},
	doi = {10.1145/3267323.3268959},
	unique-id = {30404719},
	year = {2018},
	pages = {14-27}
}

@article{MTMT:27473218,
	title = {Gépi tanulási módszerek alkalmazása deanonimizálásra},
	url = {https://m2.mtmt.hu/api/publication/27473218},
	author = {Gulyás, Gábor György},
	doi = {10.22503/inftars.XVII.2017.1.5},
	journal-iso = {INF TARS},
	journal = {INFORMÁCIÓS TÁRSADALOM: TÁRSADALOMTUDOMÁNYI FOLYÓIRAT},
	volume = {7},
	unique-id = {27473218},
	issn = {1587-8694},
	year = {2017},
	pages = {72-86}
}