Cryptology ePrint Archive: Report 2003/235

Fuzzy Extractors: How to Generate Strong Keys from Biometrics and Other Noisy Data

Yevgeniy Dodis and Rafail Ostrovsky and Leonid Reyzin and Adam Smith

Abstract: We provide formal definitions and efficient secure techniques for

-- turning noisy information into keys usable for any cryptographic application, and, in particular,

-- reliably and securely authenticating biometric data.

Our techniques apply not just to biometric information, but to any keying material that, unlike traditional cryptographic keys, is (1) not reproducible precisely and (2) not distributed uniformly. We propose two primitives: a fuzzy extractor reliably extracts nearly uniform randomness R from its input; the extraction is error-tolerant in the sense that R will be the same even if the input changes, as long as it remains reasonably close to the original. Thus, R can be used as a key in a cryptographic application. A secure sketch produces public information about its input w that does not reveal w, and yet allows exact recovery of w given another value that is close to w. Thus, it can be used to reliably reproduce error-prone biometric inputs without incurring the security risk inherent in storing them.

We define the primitives to be both formally secure and versatile, generalizing much prior work. In addition, we provide nearly optimal constructions of both primitives for various measures of "closeness" of input data, such as Hamming distance, edit distance, and set difference.

Category / Keywords: applications / Fuzzy Extractors, Fuzzy Fingerprints, Randomness Extractors, Error-Correcting Codes, Biometric Authentication, Error-Tolerance, Non-Uniformity, Password-based Systems, Metric Embeddings

Publication Info: Preliminary version in Eurocrypt 2004. This version in SIAM J. Computing 38(1), 2008.

Date: received 10 Nov 2003, last revised 1 Apr 2008

Contact author: reyzin at bu edu

Available format(s): Postscript (PS) | Compressed Postscript (PS.GZ) | PDF | BibTeX Citation

Note: Clarified discussion of average min-entropy and explicitly addressed average-case extractors. Corrected many minor bugs, typos and inconsistencies.

Version: 20080401:165042 (All versions of this report)

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