Provably secure LWE encryption with smallish uniform noise and secret

Abstract

In this paper we propose the first provably secure public key encryption scheme based on the Learning with Errors (LWE) problem, in which secrets and errors are sampled uniformly at random from a relatively small set rather than from the commonly used discrete Gaussian distribution. Using a uniform distribution, instead of a Gaussian, has the potential of improving computational efficiency a great deal due to its simplicity, thus making the scheme attractive for use in practice. At the same time our scheme features the strong security guarantee of being based on the hardness of worst-case lattice problems. After presenting the construction of our scheme we prove its security and propose asymptotic parameters. Finally, we compare our scheme on several measures to one of the most efficient LWE-based encryption schemes with Gaussian noise. We show that the expected efficiency improvement is debunked, due to the large blow-up of the parameter sets involved.