Papers
Topics
Authors
Recent
Detailed Answer
Quick Answer
Concise responses based on abstracts only
Detailed Answer
Well-researched responses based on abstracts and relevant paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses
Gemini 2.5 Flash
Gemini 2.5 Flash 64 tok/s
Gemini 2.5 Pro 50 tok/s Pro
GPT-5 Medium 30 tok/s Pro
GPT-5 High 35 tok/s Pro
GPT-4o 77 tok/s Pro
Kimi K2 174 tok/s Pro
GPT OSS 120B 457 tok/s Pro
Claude Sonnet 4 37 tok/s Pro
2000 character limit reached

On the Lattice Smoothing Parameter Problem (1412.7979v1)

Published 26 Dec 2014 in cs.CC

Abstract: The smoothing parameter $\eta_{\epsilon}(\mathcal{L})$ of a Euclidean lattice $\mathcal{L}$, introduced by Micciancio and Regev (FOCS'04; SICOMP'07), is (informally) the smallest amount of Gaussian noise that "smooths out" the discrete structure of $\mathcal{L}$ (up to error $\epsilon$). It plays a central role in the best known worst-case/average-case reductions for lattice problems, a wealth of lattice-based cryptographic constructions, and (implicitly) the tightest known transference theorems for fundamental lattice quantities. In this work we initiate a study of the complexity of approximating the smoothing parameter to within a factor $\gamma$, denoted $\gamma$-${\rm GapSPP}$. We show that (for $\epsilon = 1/{\rm poly}(n)$): $(2+o(1))$-${\rm GapSPP} \in {\rm AM}$, via a Gaussian analogue of the classic Goldreich-Goldwasser protocol (STOC'98); $(1+o(1))$-${\rm GapSPP} \in {\rm coAM}$, via a careful application of the Goldwasser-Sipser (STOC'86) set size lower bound protocol to thin spherical shells; $(2+o(1))$-${\rm GapSPP} \in {\rm SZK} \subseteq {\rm AM} \cap {\rm coAM}$ (where ${\rm SZK}$ is the class of problems having statistical zero-knowledge proofs), by constructing a suitable instance-dependent commitment scheme (for a slightly worse $o(1)$-term); $(1+o(1))$-${\rm GapSPP}$ can be solved in deterministic $2{O(n)} {\rm polylog}(1/\epsilon)$ time and $2{O(n)}$ space. As an application, we demonstrate a tighter worst-case to average-case reduction for basing cryptography on the worst-case hardness of the ${\rm GapSPP}$ problem, with $\tilde{O}(\sqrt{n})$ smaller approximation factor than the ${\rm GapSVP}$ problem. Central to our results are two novel, and nearly tight, characterizations of the magnitude of discrete Gaussian sums.

Citations (27)
List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

Summary

We haven't generated a summary for this paper yet.

Dice Question Streamline Icon: https://streamlinehq.com

Follow-Up Questions

We haven't generated follow-up questions for this paper yet.