Robust and Reusable Fuzzy Extractors for Low-entropy Rate Randomness Sources

Fuzzy extractors (FE) are cryptographic primitives that extract reliable cryptographic key from noisy real world random sources such as biometric sources. The FE generation algorithm takes a source sample, extracts a key and generates some helper data that will be used by the reproduction algorithm to recover the key. Reusability of FE guarantees that security holds when FE is used multiple times with the same source, and robustness of FE requires tampering with the helper data be detectable. In this paper, we consider information theoretic FEs, define a strong notion of reusability, and propose strongly robust and reusable FEs (srrFE) that provides the strongest combined notion of reusability and robustness for FEs. We give two constructions, one for reusable FEs and one for srrFE with information theoretic (IT) security for structured sources. The constructions are for structured sources and use sample-then-lock approach. We discuss each construction and show their unique properties in relation to existing work. Construction 2 is the first robust and reusable FE with IT-security without assuming random oracle. The robustness is achieved by using an IT-secure MAC with security against key-shift attack, which can be of independent interest.