Overcoming weak expectations

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Recently, there has been renewed interest in basing cryptographic primitives on weak secrets, where the only information about the secret is some non-trivial amount of (min-) entropy. From a formal point of view, such results require to upper bound the expectation of some function f(X), where X is a weak source in question. We show an elementary inequality which essentially upper bounds such 'weak expectation' by two terms, the first of which is independent of f, while the second only depends on the 'variance' of f under uniform distribution. Quite remarkably, as relatively simple corollaries of this elementary inequality, we obtain some 'unexpected' results, in several cases noticeably simplifying/improving prior techniques for the same problem. Examples include non-malleable extractors, leakage-resilient symmetric encryption, alternative to the dense model theorem, seed-dependent condensers and improved entropy loss for the leftover hash lemma.

Original languageEnglish (US)
Title of host publicationTheory of Cryptography - 10th Theory of Cryptography Conference, TCC 2013, Proceedings
PublisherSpringer Verlag
Pages1-22
Number of pages22
ISBN (Print)9783642365935
DOIs
StatePublished - 2013
Event10th Theory of Cryptography Conference, TCC 2013 - Tokyo, Japan
Duration: Mar 3 2013Mar 6 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7785 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other10th Theory of Cryptography Conference, TCC 2013
CountryJapan
CityTokyo
Period3/3/133/6/13

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Dodis, Y., & Yu, Y. (2013). Overcoming weak expectations. In Theory of Cryptography - 10th Theory of Cryptography Conference, TCC 2013, Proceedings (pp. 1-22). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7785 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-642-36594-2_1