A trivial debiasing scheme for Helper Data Systems

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.