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 and two-pass von Neumann algorithms.
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 language | English |
|---|---|
| Pages (from-to) | 341-349 |
| Number of pages | 9 |
| Journal | Journal of Cryptographic Engineering |
| Volume | 8 |
| Issue number | 4 |
| Early online date | 2018 |
| DOIs | |
| Publication status | Published - 1 Nov 2018 |
Keywords
- Debiasing
- Fuzzy extractor
- PUF