TY - GEN
T1 - Indistinguishability of biometric honey templates
T2 - 2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020
AU - Martiri, Edlira
AU - Yang, Bian
AU - Fauzi, Muhammad Ali
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/12
Y1 - 2020/12
N2 - In high level security environments, data protection and leakage prevention remains one of the main challenges. In biometric systems, its most sensitive piece of information, the template, is constantly being exchanged between its building blocks. instead of having one template, in this paper we generate a set of synthetic templates to camouflage the genuine one. To test their indistinguishability, we suppose an attack and compare two different classifications results of reconstructed faces: humans and SVM classifier. For the former, we built a platform where testers could classify a set of random pre-images reconstructed from real or synthetic (honey) templates. From an attacker point of view, we noticed that, compared to the SVM classifier, human testers showed better results in terms of classification distinguishability.
AB - In high level security environments, data protection and leakage prevention remains one of the main challenges. In biometric systems, its most sensitive piece of information, the template, is constantly being exchanged between its building blocks. instead of having one template, in this paper we generate a set of synthetic templates to camouflage the genuine one. To test their indistinguishability, we suppose an attack and compare two different classifications results of reconstructed faces: humans and SVM classifier. For the former, we built a platform where testers could classify a set of random pre-images reconstructed from real or synthetic (honey) templates. From an attacker point of view, we noticed that, compared to the SVM classifier, human testers showed better results in terms of classification distinguishability.
KW - biometric honey template
KW - database leakage
KW - face recognition
KW - human classification
KW - SVM
UR - https://www.scopus.com/pages/publications/85113398418
U2 - 10.1109/CSCI51800.2020.00020
DO - 10.1109/CSCI51800.2020.00020
M3 - Conference contribution
AN - SCOPUS:85113398418
T3 - Proceedings - 2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020
SP - 76
EP - 82
BT - Proceedings - 2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 16 December 2020 through 18 December 2020
ER -