LWE with sub-exponential ratio ⇒ LWE with superpolynomial ratio

Fully-black-box reduction · standard model · folklore · security loss: none

Statement

Hardness of LWE with sub-exponential modulus-to-noise ratio is hardness of LWE with superpolynomial modulus-to-noise ratio for the ratio $r(n)=2^{n^{\epsilon }}=n^{\omega (1)}$, with the same modulus and the same advantage — folklore.