Szilvia Lestyán

PhD student

lestyan (at) crysys.hu

web: www.crysys.hu/~lestyan/
office: I.E. 429
tel: +36 1 463 2063

Current courses | Publications

Current Courses

IT Security Laboratory (VIHIMB01)

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.

Publications

2019

Automatic Driver Identification from In-Vehicle Network Logs

M. Remeli, Sz. Lestyán, G. Ács, G. Biczók

22th IEEE Intelligent Transportation Systems Conference (ITSC), IEEE, 2019.

Bibtex

@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}
}

Abstract

Extracting vehicle sensor signals from CAN logs for driver re-identification

Sz. Lestyán, G. Ács, G. Biczók, Zs. Szalay

5th International Conference on Information Security and Privacy (ICISSP 2019), SCITEPRESS, 2019, shortlisted for Best Student Paper Award.

Bibtex | Abstract

@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}
}

Abstract

Data is the new oil for the car industry. Cars generate data about how they are used and who’s behind the wheel which gives rise to a novel way of profiling individuals. Several prior works have successfully demonstrated the feasibility of driver re-identification using the in-vehicle network data captured on the vehicle’s CAN bus. However, all of them used signals (e.g., velocity, brake pedal or accelerator position) that have already been extracted from the CAN log which is itself not a straightforward process. Indeed, car manufacturers intentionally do not reveal the exact signal location within CAN logs. Nevertheless, we show that signals can be efficiently extracted from CAN logs using machine learning techniques. We exploit that signals have several distinguishing statistical features which can be learnt and effectively used to identify them across different vehicles, that is, to quasi ”reverse-engineer” the CAN protocol. We also demonstrate that the extracted signals can be successfully used to re-identify individuals in a dataset of 33 drivers. Therefore, hiding signal locations in CAN logs per se does not prevent them to be regarded as personal data of drivers.

2016

Privacy Preserving Data Aggregation over Multi-hop Networks

Sz. Lestyán

Infocommunications Journal, pp. 7-15, December 2016, Volume VIII, Number 4, ISSN 2061-2079, 2016.

Bibtex

@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}
}

Abstract

2014

Efficient Apriori Based Algorithms for Privacy Preserving Frequent Itemset Mining

Sz. Lestyán, A. Csiszárik, A. Lukács

Cognitive Infocommunications (CogInfoCom), 2014 5th IEEE Conference on Cognitive Infocommunications, 2014.

Bibtex | Abstract

@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}
}

Abstract

Frequent Itemset Mining as one of the principal routine of data analysis and a basic tool of large scale information aggregation also bears a serous interest in Privacy Preserving Data Mining. In this paper Apriori based distributed, privacy preserving Frequent Itemset Mining algorithms are considered. Our secure algorithms are designed to fit in the Secure Multiparty Computation model of privacy preserving computation.