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Robustly Reusable Fuzzy Extractor from Standard Assumptions
Authors: Yunhua Wen, Shengli LiuAbstract:
A fuzzy extractor (FE) aims at deriving and reproducing (almost) uniform cryptographic keys from noisy non-uniform sources. To reproduce an identical key R from subsequent readings of a noisy source, it is necessary to eliminate the noises from those readings. To this end, a public helper string P, together with the key R, is produced from the first reading of the source during the initial enrollment phase. In this paper, we consider computational fuzzy extractor. We formalize robustly reusable fuzzy extractor (rrFE) which considers reusability and robustness simultaneously in the Common Reference String (CRS) model. Reusability of rrFE deals with source reuse. It guarantees that the key R output by fuzzy extractor is pseudo-random even if the initial enrollment is applied to the same source several times, generating multiple public helper strings and keys (P_i, R_i). Robustness of rrFE deals with active probabilistic polynomial-time adversaries, who may manipulate the public helper string P_i to affect the reproduction of R_i. Any modification of P_i by the adversary will be detected by the robustness of rrFE.
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