A trivial debiasing scheme for Helper Data Systems

Boris Skoric

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: PUF fuzzy 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: 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},
      url = {https://eprint.iacr.org/2016/241}
}