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 language | English |
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| Media of output | IACR eprint archive |
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| Number of pages | 10 |
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| Publication status | Published - 2016 |
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