## CryptoDB

### Paper: Hardness of LWE on General Entropic Distributions

Authors: Zvika Brakerski , Weizmann Institute of Science Nico Döttling , CISPA Helmholtz Center DOI: 10.1007/978-3-030-45724-2_19 (login may be required) Search ePrint Search Google EUROCRYPT 2020 The hardness of the Learning with Errors (LWE) problem is by now a cornerstone of the cryptographic landscape, allowing to con- struct cryptographic schemes with properties unknown under other as- sumptions, and being conjectured to be resilient to quantum attacks. LWE is essentially the task of solving a noisy system of random linear equations over uniformly random secret variables (“the LWE secret”), evaluated modulo some integer. In applications the secret variables usu- ally correspond to the secret key of the cryptographic scheme. It is therefore of great importance to understand what happens when the secret variables are not sampled uniformly (but still have some entropy). This is relevant for settings where an adversary manages to obtain partial information on the secret (a.k.a key leakage), for various theoretical ap- plications, and also for practical use where for efficiency or convenience it is easier to sample the secret from some non-uniform distribution. This so called “Entropic LWE” problem has been studied in a number of works, starting with Goldwasser et al. (ICS 2010). However, so far it was only known how to prove the hardness of Entropic LWE for secret distributions supported inside a ball of small radius. In this work we resolve the hardness of Entropic LWE with arbitrary long secrets, in the following sense. We show an entropy bound that guarantees the security of arbitrary Entropic LWE. This bound is higher than what is required in the ball-bounded setting, but we show that this is essentially tight. Tightness is shown unconditionally for highly-composite moduli, and using black-box impossibility for arbitrary moduli. Technically, we show that the entropic hardness of LWE relies on a sim- ple to describe lossiness property of the distribution of secrets itself. This is simply the probability of recovering a random sample from this distri- bution s, given s + e, where e is Gaussian noise (i.e. the quality of the distribution of secrets as an error correcting code for Gaussian noise). We hope that this characterization will make it easier to derive entropic LWE results more easily in the future. We also use our techniques to show new results for the ball-bounded setting, essentially showing that under a strong enough assumption even polylogarithmic entropy suffices.
##### BibTeX
@inproceedings{eurocrypt-2020-30241,
title={Hardness of LWE on General Entropic Distributions},
booktitle={39th Annual International Conference on the Theory and Applications of Cryptographic Techniques, Zagreb, Croatia, May 10–14, 2020, Proceedings},
series={Lecture Notes in Computer Science},
publisher={Springer},
keywords={learning with errors;entropic secrets},
volume={12105},
doi={10.1007/978-3-030-45724-2_19},
author={Zvika Brakerski and Nico Döttling},
year=2020
}