Theory of Computing
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Title : On the Hardness of Learning With Errors with Binary Secrets
Authors : Daniele Micciancio
Volume : 14
Number : 13
Pages : 1-17
URL : http://www.theoryofcomputing.org/articles/v014a013
Abstract
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We give a simple proof that the decisional Learning With Errors (LWE)
problem with binary secrets (and an arbitrary polynomial number of
samples) is at least as hard as the standard LWE problem (with
unrestricted, uniformly random secrets, and a bounded, quasi-linear
number of samples). This proves that the binary-secret LWE
distribution is pseudorandom, under standard worst-case complexity
assumptions on lattice problems. Our results are similar to those
proved by Brakerski, Langlois, Peikert, Regev and Stehle (STOC 2013),
but provide a shorter, more direct proof, and a small improvement in
the noise growth of the reduction.