Discrete Gaussian sampling is a fundamental building block of lattice-based cryptography. Sampling from a Gaussian distribution ℤ, , over the integers ℤ is an important sub-problem of discrete Gaussian sampling, where parameter σ > 0 and center c ∈ ℝ. In this paper, we show that two common sampling algorithms for discrete Gaussian distribution over the integers can be implemented more efficiently by using vectorization with SIMD (Single Instruction Multiple Data) support. Specifically, we use the VCL (C++ vector class library) by Agner Fog, which offers optimized vector operations for integers and floating-point numbers with the support of SIMD. The VCL is also a simple tool for constant-time implementations, which helps prevent the information leakage caused by the timing attacks on sampling operations.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.