A Robust Fuzzy Extractor without ECCs

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• 作者：Jintao Yao (18)
  Kangshun Li (18)
  Mingwu Zhang (18)
  Min Zhou (18)
  
• 关键词：Biometric data ; error correction code ; robustness ; fuzzy extractor ; noise patterns
• 刊名：Lecture Notes in Computer Science
• 出版年：2013
• 出版时间：2013
• 年：2013
• 卷：7763
• 期：1
• 页码：69-89
• 全文大小：180KB
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• 作者单位：Jintao Yao (18)
  Kangshun Li (18)
  Mingwu Zhang (18)
  Min Zhou (18)
  
  18. College of Informatics, South China Agricultural University, Guangzhou, 510642, China
  
• ISSN：1611-3349

文摘

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??-em class="a-plus-plus">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.