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Authors: Diana Batista 1 ; Helena Aidos 2 ; Ana Fred 1 ; Joana Santos 3 ; Rui Cruz Ferreira 4 and Rui César das Neves 5

Affiliations: 1 Instituto de Telecomunicações and Universidade de Lisboa, Portugal ; 2 Instituto de Telecomunicações, Portugal ; 3 Cruz Vermelha Portuguesa, Portugal ; 4 Hospital de Santa Marta, Portugal ; 5 CAST - Cons. e Apl. em Sistemas e Tecnologias and Lda, Portugal

ISBN: 978-989-758-279-0

Keyword(s): ECG, Biometrics, Dissimilarity Representation, Dissimilarity Increments, Cloud-based System.

Related Ontology Subjects/Areas/Topics: Applications ; Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Biometrics ; Biometrics and Pattern Recognition ; Data Manipulation ; Detection and Identification ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Methodologies and Methods ; Multimedia ; Multimedia Signal Processing ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Soft Computing ; Telecommunications

Abstract: Biometric recognition has become a popular approach for user identification and authentication. However, since in ECG-based biometrics users cannot change their authentication/identification signal (unlike in password-based methods), its applicability is seriously constrained for cloud-based systems: a hacker could potentially retrieve the stored ECG signal, eternally disabling ECG-based biometrics for the attacked user. To overcome such an issue, new methodologies must be devised to enable cloud-based authentication/ identification systems without requiring the transmission and storage of the user’s ECG signal on remote servers. In this paper we propose an ECG biometric approach that relies on non-linear irreversible dissimilarity spaces to encode (encrypt) the user’s ECG. We show how to construct the dissimilarity space, and also evaluate the system’s accuracy with the dimensionality of the dissimilarity space. We show that the proposed biometric system retains similar identificatio n errors as an equivalent system relying on the Euclidean space, while the latter can potentially be broken by using triangulation techniques to uncover the users original ECG signal. (More)

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Paper citation in several formats:
Batista D., Aidos H., Fred A., Santos J., Ferreira R. and das Neves R. (2018). Protecting the ECG Signal in Cloud-based User Identification System - A Dissimilarity Representation Approach.In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOSIGNALS, ISBN 978-989-758-279-0, pages 78-86. DOI: 10.5220/0006723900780086

@conference{biosignals18,
author={Diana Batista and Helena Aidos and Ana Fred and Joana Santos and Rui Cruz Ferreira and Rui César das Neves},
title={Protecting the ECG Signal in Cloud-based User Identification System - A Dissimilarity Representation Approach},
booktitle={Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOSIGNALS,},
year={2018},
pages={78-86},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006723900780086},
isbn={978-989-758-279-0},
}

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOSIGNALS,
TI - Protecting the ECG Signal in Cloud-based User Identification System - A Dissimilarity Representation Approach
SN - 978-989-758-279-0
AU - Batista D.
AU - Aidos H.
AU - Fred A.
AU - Santos J.
AU - Ferreira R.
AU - das Neves R.
PY - 2018
SP - 78
EP - 86
DO - 10.5220/0006723900780086

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