Middle-Product Learning with Rounding Problem and its Applications

This talk focuses on a new variant of the Learning With Errors (LWE) problem, a fundamental computational problem used in lattice-based cryptography.<br/> At Crypto17, Roşca et al. introduced the Middle-Product LWE problem (MP-LWE), whose hardness is based on the hardness of the Polynomial LWE (P-LWE) problem parameterized by a large set of polynomials, making it more secure against the possible weakness of a single defining polynomial. As a cryptographic application, they also provided an encryption scheme based on the MP-LWE problem. In this talk, I present a deterministic variant of their encryption scheme, which does not need Gaussian sampling and is thus simpler than the original one. Still, it has the same quasi-optimal asymptotic key and ciphertext sizes. The hardness of the scheme is based on a new assumption called Middle-Product Computational Learning With Rounding. We prove that this new assumption is as hard as the decisional version of MP-LWE and thus benefits from worst-case to average-case hardness guarantees.<br/> lien: http://e-learning.sviesolutions.com/4bl7vxoqql0b