A trivial debiasing scheme for helper data systems

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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.
Original languageEnglish
Pages (from-to)341-349
Number of pages9
JournalJournal of Cryptographic Engineering
Issue number4
Early online date2018
Publication statusPublished - 1 Nov 2018


  • Debiasing
  • Fuzzy extractor
  • PUF


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