Welcome to the resource topic for 2022/615
Title:
Smoothing Codes and Lattices: Systematic Study and New Bounds
Authors: Thomas Debris-Alazard, Léo Ducas, Nicolas Resch, Jean-Pierre Tillich
Abstract:In this article we revisit smoothing bounds in parallel between lattices \emph{and} codes. Initially introduced by Micciancio and Regev, these bounds were instantiated with Gaussian distributions and were crucial for arguing the security of many lattice-based cryptosystems. Unencumbered by direct application concerns, we provide a systematic study of how these bounds are obtained for both lattices \emph{and} codes, transferring techniques between both areas. We also consider various spherically symmetric noise distributions. We found that the best strategy for a worst-case bound combines Parseval’s Identity, the Cauchy-Schwarz inequality, and the second linear programming bound, and this for both codes and lattices, and for all noise distributions at hand. For an average-case analysis, the linear programming bound can be replaced by a tight average count. This alone gives optimal results for spherically uniform noise over random codes and random lattices. This also improves previous Gaussian smoothing bound for worst-case lattices, but surprisingly this provides even better results for uniform noise than for Gaussian (or Bernouilli noise for codes). This counter-intuitive situation can be resolved by adequate decomposition and truncation of Gaussian and Bernouilli distribution into a superposition of uniform noise, giving further improvement for those cases, and putting them on par with the uniform cases.
ePrint: https://eprint.iacr.org/2022/615
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