Fingerprint-Based Fuzzy Vault: Implementation and Performance

By Karthik Nandakumar, Anil K. Jain, Sharath Pankanti. IEEE Transactions on Information Forensics and Security, 2007

631 citations recorded by the source corpus.

Reliable information security mechanisms are required to combat the rising magnitude of identity theft in our society. While cryptography is a powerful tool to achieve information security, one of the main challenges in cryptosystems is to maintain the secrecy of the cryptographic keys. Though biometric authentication can be used to ensure that only the legitimate user has access to the secret keys, a biometric system itself is vulnerable to a number of threats. A critical issue in biometric systems is to protect the template of a user which is typically stored in a database or a smart card. The fuzzy vault construct is a biometric cryptosystem that secures both the secret key and the biometric template by binding them within a cryptographic framework. We present a fully automatic implementation of the fuzzy vault scheme based on fingerprint minutiae. Since the fuzzy vault stores only a transformed version of the template, aligning the query fingerprint with the template is a challenging task. We extract high curvature points derived from the fingerprint orientation field and use them as helper data to align the template and query minutiae. The helper data itself do not leak any information about the minutiae template, yet contain sufficient information to align the template and query fingerprints accurately. Further, we apply a minutiae matcher during decoding to account for nonlinear distortion and this leads to significant improvement in the genuine accept rate. We demonstrate the performance of the vault implementation on two different fingerprint databases. We also show that performance improvement can be achieved by using multiple fingerprint impressions during enrollment and verification.

Subfields: Computer Vision and Pattern Recognition, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/12d3dbe11fb18c5c62b2ff15ae42efe9b04cfaf4

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Citations in the corpus

Multibiometric Cryptosystems Based on Feature-Level Fusion (2012, methodology): “Finally, the security level of 53 bits used in Table III is higher when compared to those typically reported in the literature [19], [25].”
Cracking Fuzzy Vaults and Biometric Encryption (2007, methodology): “[8] and [ 9 ] align the query fingerprint with respect to the template via an Iterative Closest Point algorithm.”
A survey on biometric cryptosystems and cancelable biometrics (2011, background): “Recently, several techniques [56,57] to overcome the shortcoming of prealignment have been proposed.”
Biometric Template Security (2008, methodology): “We implemented the fuzzy vault as proposed in [ 89 ] using the first two impressions of each of the 100 fingers in the FVC2002-DB2.”