On Ring Learning with Errors over the Tensor Product of Number Fields
This work addresses the need for more efficient cryptographic schemes for multidimensional data, though it appears incremental as it extends existing RLWE concepts.
The paper tackles the generalization of the Ring Learning with Errors (RLWE) problem to multivariate rings, introducing the Multivariate Ring Learning with Errors (m-RLWE) problem and showing its relation to hardness problems over tensor products of ideal lattices, with the result that m-RLWE is more suitable for cryptographic applications involving multidimensional structures.
The "Ring Learning with Errors" (RLWE) problem was formulated as a variant of the "Learning with Errors" (LWE) problem, with the purpose of taking advantage of an additional algebraic structure in the underlying considered lattices; this enables improvements on the efficiency and cipher expansion on those cryptographic applications which were previously based on the LWE problem. In Eurocrypt 2010, Lyubashevsky et al. introduced this hardness problem and showed its relation to some known hardness problems over lattices with a special structure. In this work, we generalize these results and the problems presented by Lyubashevsky et al. to the more general case of multivariate rings, highlighting the main differences with respect to the security proof for the RLWE counterpart. This hardness problem is denoted as "Multivariate Ring Learning with Errors" ($m$-RLWE or multivariate RLWE) and we show its relation to hardness problems over the tensor product of ideal lattices. Additionally, the $m$-RLWE problem is more adequate than its univariate version for cryptographic applications dealing with multidimensional structures.