This paper presents a novel approach to feature extraction for face recognition. This approach extends a previously developed method that incorporated the feature extraction techniques of GEFE ML (Genetic and Evolutionary Feature Extraction – Machine Learning) and Darwinian Feature Extraction). The feature extractors evolved by GEFE ML are superior to traditional feature extraction methods in t...

We consider the problem of constructing randomness extractors that are locally computable; that is, read only a small number of bits from their input. As recently shown by Lu (CRYPTO ‘02 ), locally computable extractors directly yield secure private-key cryptosystems in Maurer’s bounded storage model (J. Cryptology, 1992). We suggest a general “sample-then-extract” approach to constructing loca...

Randomness extractors are an important building block for classical and quantum cryptography. However, for many applications it is crucial that the extractors are quantum-proof, i.e., that they work even in the presence of quantum adversaries. In general, quantum-proof extractors are poorly understood and we would like to argue that in the same way as Bell inequalities (multi prover games) and ...

We present a negative result of fuzzy extractors with computational security. Specifically, we show that, under a certain computational condition, the existence of a computational fuzzy extractor implies the existence of an information-theoretic fuzzy extractor with slightly weaker parameters. The condition is that the generation procedure of the fuzzy extractor is efficiently invertible by an ...