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LWE

Learning With Errors

Recover or distinguish a noisy linear system.

BKW · q ≍ n², width ≍ n · unlimited samples

Uniform-secret LWE, prime q=Θ(n²), discrete-Gaussian width s=Θ(n); Unlimited fresh samples

2 results · 2 papers
ClassicalProvableUniform random equations2c n + o(n)
#PUBLICATION / AUTHORSDATETIME BOUNDSPACE BOUNDEVIDENCE
1An Improved BKW Algorithm for LWE with Applications to Cryptography and LatticesPaul Kirchner · Pierre-Alain FouqueCrypto 2015SourceBibTeX / detailsclassical provable LWE BKW29 Jun 20152(1/(1/2+2 ln 2)+o(1)) n2(1/(1/2+2 ln 2)+o(1)) nPublishedSource checked
2On the Asymptotic Complexity of Solving LWEGottfried Herold · Elena Kirshanova · Alexander MayDesigns, Codes and Cryptography 2018SourceBibTeX / detailsclassical provable LWE BKW20182(2/[2(2−1+1/2)]+o(1)) n2(2/[2(2−1+1/2)]+o(1)) nPublishedSource checked
Comparison definition & assumptions
Regime
Uniform-secret LWE, prime q=Θ(n²), discrete-Gaussian width s=Θ(n); Unlimited fresh samples
Computation
Classical
Rigor
Provable
Instances
Uniform random equations
Cost scale
2^{c n + o(n)}
Parameter
n
Description
Uniform-secret LWE, prime q=Θ(n²), discrete-Gaussian width s=Θ(n); Unlimited fresh samples
Comparison rules
Use q=Θ(n²), Gaussian width s=Θ(n), and exactly the displayed asymptotic sample budget. Constant Gaussian normalizations do not change these exponents. These are interior parameters c_q=2,c_s=1; they are not the Regev q≈n²,width≈n^{3/2}/polylog(n) regime.
Tags
BKW, Gaussian noise, sample budget