A practical fuzzy extractor for continuous features

Abstract

Many fuzzy extractors have been presented for discrete data; here we present a fuzzy extractor for continuous data. Our approach uses the code-offset method extended to ℝn by using lattice codes and Euclidean distance. This is accomplished in the Unconstrained Power Channel, a theoretical artifact especially developed for lattice codes used in scenarios other than telecommunication, in which the noise is assumed to be white Gaussian. To prove security we give a lower bound on the min-entropy of the common secret that an adversary necessarily faces; we also provide an upper bound. In addition we present a construction using Low-Density Lattice Codes. Our construction is more practical than existing proposals since it can be used with a feature of any dimension n and with some noise distributions that are not white Gaussian inherent to that feature.