2016-11-09
AUTHORSBenjamin Fuller , Leonid Reyzin , Adam Smith
ABSTRACTFuzzy extractors (Dodis et al., Eurocrypt 2004) convert repeated noisy readings of a high-entropy secret into the same uniformly distributed key. A minimum condition for the security of the key is the hardness of guessing a value that is similar to the secret, because the fuzzy extractor converts such a guess to the key.We define fuzzy min-entropy to quantify this property of a noisy source of secrets. Fuzzy min-entropy measures the success of the adversary when provided with only the functionality of the fuzzy extractor, that is, the ideal security possible from a noisy distribution. High fuzzy min-entropy is necessary for the existence of a fuzzy extractor.We ask: is high fuzzy min-entropy a sufficient condition for key extraction from noisy sources? If only computational security is required, recent progress on program obfuscation gives evidence that fuzzy min-entropy is indeed sufficient. In contrast, information-theoretic fuzzy extractors are not known for many practically relevant sources of high fuzzy min-entropy.In this paper, we show that fuzzy min-entropy is sufficient for information theoretically secure fuzzy extraction. For every source distribution W for which security is possible we give a secure fuzzy extractor.Our construction relies on the fuzzy extractor knowing the precise distribution of the source W. A more ambitious goal is to design a single extractor that works for all possible sources. Our second main result is that this more ambitious goal is impossible: we give a family of sources with high fuzzy min-entropy for which no single fuzzy extractor is secure. We show three flavors of this impossibility result: for standard fuzzy extractors, for fuzzy extractors that are allowed to sometimes be wrong, and for secure sketches, which are the main ingredient of most fuzzy extractor constructions. More... »
PAGES277-306
Advances in Cryptology – ASIACRYPT 2016
ISBN
978-3-662-53886-9
978-3-662-53887-6
http://scigraph.springernature.com/pub.10.1007/978-3-662-53887-6_10
DOIhttp://dx.doi.org/10.1007/978-3-662-53887-6_10
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1084920151
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