Cryptology ePrint Archive: Report 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 von Neumann algorithm.

Category / Keywords: PUF, fuzzy extractor

Date: received 4 Mar 2016, last revised 4 Mar 2016

Contact author: b skoric at tue nl

Available format(s): PDF | BibTeX Citation

Version: 20160304:154143 (All versions of this report)

Short URL: ia.cr/2016/241

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