CryptoDB
Robustly Reusable Fuzzy Extractor from Standard Assumptions
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| Conference: | ASIACRYPT 2018 |
| Abstract: | 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. We show how to construct an rrFE from a Symmetric Key Encapsulation Mechanism (SKEM), a Secure Sketch (SS), an Extractor (Ext), and a Lossy Algebraic Filter (LAF). We characterize the key-shift security notion of SKEM and the homomorphic properties of SS, Ext and LAF, which enable our construction of rrFE to achieve both reusability and robustness.We present an instantiation of SKEM from the DDH assumption. Combined with the LAF by Hofheinz (EuroCrypt 2013), homomorphic SS and Ext, we obtain the first rrFE based on standard assumptions. |
BibTeX
@inproceedings{asiacrypt-2018-29198,
title={Robustly Reusable Fuzzy Extractor from Standard Assumptions},
booktitle={Advances in Cryptology – ASIACRYPT 2018},
series={Lecture Notes in Computer Science},
publisher={Springer},
volume={11274},
pages={459-489},
doi={10.1007/978-3-030-03332-3_17},
author={Yunhua Wen and Shengli Liu},
year=2018
}