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Conference Papers Year : 2024

Fuzzy Vault Security Enhancement avoid Statistical Biases

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.
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Dates and versions

hal-04563794 , version 1 (30-04-2024)

Identifiers

  • HAL Id : hal-04563794 , version 1

Cite

Sara Majbour, Morgan Barbier, Jean-Marie Le Bars. Fuzzy Vault Security Enhancement avoid Statistical Biases. SECRYPT, Jul 2024, Dijon, France. ⟨hal-04563794⟩
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