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Published in:
2013 | OriginalPaper | Chapter
A Robust Fuzzy Extractor without ECCs
Authors : Jintao Yao, Kangshun Li, Mingwu Zhang, Min Zhou
Published in: Information Security and Cryptology
Publisher: Springer Berlin Heidelberg
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Fuzzy extractors are important secure schemes that are used to extract reliably reproducible uniform randomness from noise and biased biometric data. It is well known that the error-correction codes (ECCs) only work best when the noise patterns likely to occur are completely random, and typical error-correction codes are unable to capitalize on the errors with low entropy. Consequently, a new robust fuzzy extractor without ECCs was proposed and constructed. Our fuzzy extractor adopts the error-correction method based on incoming (1 −
θ
)-neighborhood and extracts more min-entropy under the condition of not-uniform noise patterns than those using standard error-correction codes. In addition, we also analyzed the proposed scheme’s correctness and efficiency, and proved its security and robustness mainly. The result shows that the proposed fuzzy extractor has the better practical value.