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We provide formal definitions and efficient secure techniques for  - turning noisy information into keys usable for any cryptographicapplication, and, in particular,  - reliably and securely authenticating biometric data.  Our techniques apply not just to biometric information, but to any keyingmaterial that, unlike traditional cryptographic keys, is (1) not reproducibleprecisely 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 ifthe 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 yetallows exact recovery of w given another value that is close to w. Thus, it canbe used to reliably reproduce error-prone biometric inputs without incurringthe 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 optimalconstructions of both primitives for various measures of ``closeness'' of inputdata, such as Hamming distance, edit distance, and set difference.Comment: 47 pp., 3 figures. Prelim. version in Eurocrypt 2004, Springer LNCS  3027, pp. 523-540. Differences from version 3: minor edits for grammar,  clarity, and typo

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