Statistical attack against fuzzy commitment scheme
作者： C. Rathgeb , A. Uhl
DOI: 10.1049/IET-BMT.2011.0001
关键词: Binary number 、 Feature vector 、 Bounded function 、 Fuzzy logic 、 Error detection and correction 、 Entropy (information theory) 、 Binomial distribution 、 Biometrics 、 Data mining 、 Computer science
摘要: In this study a statistical attack against fuzzy commitment schemes is presented. Comparisons of different pairs binary biometric feature vectors yield binomial distributions, the standard deviations which are bounded by entropy templates. case error correction consists series chunks, like in vast majority approaches, helper data become vulnerable to attacks. Error-correction codewords bound separate parts template among dispersed. As consequence, chunks prone significant false acceptance. experimental evaluations proposed applied iris-biometric retrieving cryptographic keys at alarming low effort.
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