Paper 2016/241

A trivial debiasing scheme for Helper Data Systems

Boris Skoric

Abstract

We introduce a debiasing scheme that solves the more-noise-than-entropy problem which can occur in Helper Data Systems when the source is very biased. We perform a condensing step, similar to Index Based Syndrome coding, that reduces the size of the source space in such a way that some source entropy is lost while the noise entropy is greatly reduced. In addition, our method allows for even more entropy extraction by means of a `spamming' technique. Our method outperforms solutions based on the one-pass and two-pass von Neumann algorithms.

Metadata
Available format(s)
PDF
Publication info
Preprint. MINOR revision.
Keywords
PUFfuzzy extractor
Contact author(s)
b skoric @ tue nl
History
2018-02-18: revised
2016-03-04: received
See all versions
Short URL
https://ia.cr/2016/241
License
Creative Commons Attribution
CC BY

BibTeX

@misc{cryptoeprint:2016/241,
      author = {Boris Skoric},
      title = {A trivial debiasing scheme for Helper Data Systems},
      howpublished = {Cryptology ePrint Archive, Paper 2016/241},
      year = {2016},
      note = {\url{https://eprint.iacr.org/2016/241}},
      url = {https://eprint.iacr.org/2016/241}
}
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