**Estimation of the Hardness of the Learning with Errors Problem with a Restricted Number of Samples**

*Markus Schmidt and Nina Bindel*

**Abstract: **Lattice-based cryptography is a promising candidate to build cryptographic primitives that are secure against quantum algorithms. The Learning with Errors problem is one of the most important hardness assumptions, lattice-based constructions base their security on. Recently, Albrecht et al. (Journal of Mathematical Cryptology, 2015) presented the Sage module LWE-Estimator to estimate the hardness of concrete LWE instances, making the choice of parameters for lattice-based primitives easier and better comparable. The effectiveness of algorithms to solve LWE is often depending on the number of LWE instances, called LWE samples, given. To give lower bounds on the hardness of concrete instances it is assumed that each algorithm has given enough samples to run in optimal runtime per instance. That means it is assumed that the optimal number of samples is available. However, in cryptographic applications the optimal number of samples is often not given, but only a small number of samples. This leads to a more conservative choice of parameters than necessary in applications.

This work aims to improve the parameter choice with respect to the described problem. Our contribution is twofold: First, we analyze the hardness of LWE instances given a restricted number of samples. For this, we describe algorithms proposed in the literature to solve LWE briefly and estimate their computational cost while taking a restricted number of samples into account. Secondly, we extend the Sage module LWE-Estimator, based on our theoretical results. Furthermore, we evaluate the resulting implementation and show that restricting the number of samples has a significant impact on the hardness of LWE instances.

**Category / Keywords: **public-key cryptography / learning with errors, hardness estimation, lattice-based cryptography

**Date: **received 15 Feb 2017

**Contact author: **nbindel at cdc informatik tu-darmstadt de

**Available format(s): **PDF | BibTeX Citation

**Version: **20170220:145454 (All versions of this report)

**Short URL: **ia.cr/2017/140

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