Fuzzy Extractors: How to Generate Strong Keys From Biometrics and Other Noisy Data
Source: New York University
This paper provides 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. The techniques apply not just to biometric information, but to any keying material that, unlike traditional cryptographic keys, is not reproducible precisely and not distributed uniformly. This paper proposes 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.