Robust and Reusable Fuzzy Extractors for Low-entropy Rate Randomness Sources
arxiv(2024)
摘要
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.
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