A trivial debiasing scheme for helper data systems

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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 von Neumann algorithm.
Original languageEnglish
Media of outputIACR eprint archive
Number of pages10
Publication statusPublished - 2016

Publication series

NameIACR eprint archive

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