Pseudorandom knapsacks and the sample complexity of LWE search-to-decision reductions

  • Authors:
  • Daniele Micciancio;Petros Mol

  • Affiliations:
  • Department of Computer Science & Engineering, University of California, San Diego;Department of Computer Science & Engineering, University of California, San Diego

  • Venue:
  • CRYPTO'11 Proceedings of the 31st annual conference on Advances in cryptology
  • Year:
  • 2011

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Abstract

We study the pseudorandomness of bounded knapsack functions over arbitrary finite abelian groups. Previous works consider only specific families of finite abelian groups and 0-1 coefficients. The main technical contribution of our work is a new, general theorem that provides sufficient conditions under which pseudorandomness of bounded knapsack functions follows directly from their one-wayness. Our results generalize and substantially extend previous work of Impagliazzo and Naor (J. Cryptology 1996). As an application of the new theorem, we give sample preserving search-to-decision reductions for the Learning With Errors (LWE) problem, introduced by (Regev, STOC 2005) and widely used in lattice-based cryptography. Concretely, we show that, for a wide range of parameters, m LWE samples can be proved indistinguishable from random just under the hypothesis that search LWE is a one-way function for the same number m of samples.