You are looking at a specific version 20160304:154143 of this paper.
See the latest version.
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 von Neumann algorithm.
Metadata
- Available format(s)
- Publication info
- Preprint. MINOR revision.
- Keywords
- PUFfuzzy 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