Paper 2021/1228

Computational Robust (Fuzzy) Extractors for CRS-dependent Sources with Minimal Min-entropy

Hanwen Feng and Qiang Tang


Robust (fuzzy) extractors are very useful for, e.g., authenticated key exchange from a shared weak secret and remote biometric authentication against active adversaries. They enable two parties to extract the same uniform randomness with a ``helper'' string. More importantly, they have an authentication mechanism built in that tampering of the ``helper'' string will be detected. Unfortunately, as shown by Dodis and Wichs, in the information-theoretic setting, a robust extractor for an $(n,k)$-source requires $k>n/2$, which is in sharp contrast with randomness extractors which only require $k=\omega(\log n)$. Existing works either rely on random oracles or introduce CRS and work only for CRS-independent sources (even in the computational setting). In this work, we give a systematic study about robust (fuzzy) extractors for general CRS {\em dependent} sources. We show in the information-theoretic setting, the same entropy lower bound holds even in the CRS model; we then show we {\em can} have robust extractors in the computational setting for general CRS-dependent source that is only with minimal entropy. We further extend our construction to robust fuzzy extractors. Along the way, we propose a new primitive called $\kappa$-MAC, which is unforgeable with a weak key and hides all partial information about the key (both against auxiliary input); it may be of independent interests.

Available format(s)
Publication info
Published by the IACR in Tcc 2021
Contact author(s)
fenghanwen fhw @ alibaba-inc com
qiang tang @ sydney edu au
2021-09-20: received
Short URL
Creative Commons Attribution


      author = {Hanwen Feng and Qiang Tang},
      title = {Computational Robust (Fuzzy) Extractors for CRS-dependent Sources with Minimal Min-entropy},
      howpublished = {Cryptology ePrint Archive, Paper 2021/1228},
      year = {2021},
      note = {\url{}},
      url = {}
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