Fuzzy Vault Security Enhancement Avoid Statistical Biases

Sara Majbour, Morgan Barbier, Jean-Marie Le Bars

2024

Abstract

We assess the fuzzy vault’s security against the exploitation of statistical biases, conducting bias examination through features on a sample of biometric set. Our comparative analysis quantifies the scheme’s vulnerability to security-compromising attacks, using three bases of feature templates derived from real biometric databases of various modalities, showcasing variable quality levels, and quantifying scheme weaknesses. This study shows a decrease in the scheme’s security under such attacks and significantly contributes to understanding the fuzzy vault’s limitations regarding biases in the stored set. Moreover, we propose the first solution without requiring additional information, preserving the security of the fuzzy vault against such attacks.

Paper Citation

in Harvard Style

Majbour S., Barbier M. and Le Bars J. (2024). Fuzzy Vault Security Enhancement Avoid Statistical Biases. In Proceedings of the 21st International Conference on Security and Cryptography - Volume 1: SECRYPT; ISBN 978-989-758-709-2, SciTePress, pages 40-51. DOI: 10.5220/0012715500003767

in Bibtex Style

@conference{secrypt24,
author={Sara Majbour and Morgan Barbier and Jean-Marie Le Bars},
title={Fuzzy Vault Security Enhancement Avoid Statistical Biases},
booktitle={Proceedings of the 21st International Conference on Security and Cryptography - Volume 1: SECRYPT},
year={2024},
pages={40-51},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012715500003767},
isbn={978-989-758-709-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 21st International Conference on Security and Cryptography - Volume 1: SECRYPT
TI - Fuzzy Vault Security Enhancement Avoid Statistical Biases
SN - 978-989-758-709-2
AU - Majbour S.
AU - Barbier M.
AU - Le Bars J.
PY - 2024
SP - 40
EP - 51
DO - 10.5220/0012715500003767
PB - SciTePress