A trivial debiasing scheme for Helper Data Systems

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.

• Type: Journal
• Year: 2016
• Where: IACR
• Authors: Boris Skoric
• Keywords: Biometrics, IACR 2016