A Novel Fuzzy Identity-Based Signature with Dynamic Threshold

Chen, Wei; Zhu, Li; Cao, Xiaomei; Yang, Geng

doi:10.1109/nss.2009.40

Cited by 4 publications

(2 citation statements)

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“…On the other hand, fuzzy identity‐based signature (FIBS)—the second IBC primitive—has not drawn much attention as only a few FIBS schemes have been proposed up to date. The first FIBS in the literature was proposed by Yang et al by adopting the key extraction technique of Sahai and Waters' FIBE, and the signature is generated by using the query public biometric identity ID '.…”

Section: Introductionmentioning

confidence: 99%

“…The most efficient FIBS scheme among all would be Wang and Kim's FIBS , which is claimed to be existentially unforgeable under the chosen message attack and fuzzy identity attack in the random oracle model, assuming that the discrete logarithm problem is computationally hard. On the other hand, the most flexible FIBS scheme would be the FIBS of Chen et al , which is proven secure against unforgeability in the standard model if the multi‐sequence of Diffie–Hellman exponents problem is computationally hard.…”

Section: Introductionmentioning

confidence: 99%

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On the realization of fuzzy identity‐based identification scheme using fingerprint biometrics

Tan

Jin

Teoh

et al. 2012

Security Comm Networks

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Fuzzy identity-based identification (FIBI) scheme is a recently proposed cryptographic identification protocol. The scheme utilizes user biometric trait as public keys. The authentication is deemed success in the presence of the genuine query biometric together with the valid private key. Because of the fuzziness nature of biometrics, FIBI does not correct the errors on the query biometric with respect to the public key; instead, it tolerates the errors using Lagrange polynomial interpolation. Therefore, FIBI requires the biometric trait to be represented in a discrete (binary or integer) array that is fixed in length. In this paper, we report the first realization of FIBI scheme by means of fingerprint biometrics using minutia representation where our technique integrates the security features of both biometric and cryptography effectively. The simulation shows that the entire protocol can be completed within 1 s where false acceptance rate (FAR) = 0% and false reject rate (FRR) = 0.25% in FVC2002 DB1, and FAR = 0% and FRR = 0.125% in FVC2002 DB2. Our integration technique may also be applied on other fuzzy identity-based cryptosystems.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%