A trivial debiasing scheme for helper data systems

Research output: Other contributionOther research output

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.
LanguageEnglish
Number of pages10
StatePublished - 2016

Publication series

NameIACR eprint archive

Fingerprint

data systems
entropy
condensing
coding

Cite this

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title = "A trivial debiasing scheme for helper data systems",
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.",
author = "B. Skoric",
year = "2016",
language = "English",
series = "IACR eprint archive",
type = "Other",

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A trivial debiasing scheme for helper data systems. / Skoric, B.

10 p. 2016, . (IACR eprint archive).

Research output: Other contributionOther research output

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N2 - 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.

AB - 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.

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