Answers are grounded in this paper and cite their sources.
Polynomial multiplication is the basic and most computationally intensive operation in ring-learning with errors (ring-LWE) encryption and "somewhat" homomorphic encryption (SHE) cryptosystems. In this paper, the fast Fourier transform (FFT) with a linearithmic complexity of O(nlogn), is exploited in the design of a high-speed polynomial multiplier. A constant geometry FFT datapath is used in the computation to simplify the control of the architecture. The contribution of this work is three-fold. First, parameter sets which support both an efficient modular reduction design and the security requirements for ring-LWE encryption and SHE are provided. Second, a versatile pipelined architecture accompanied with an improved dataflow are proposed to obtain a high-speed polynomial multiplier. Third, the proposed architecture supports polynomial multiplications for different lengths n and moduli p. The experimental results on a Spartan-6 FPGA show that the proposed design results in a speedup of 3.5 times on average when compared with the state of the art. It performs a polynomial multiplication in the ring-LWE scheme (n=256,p=1049089) and the SHE scheme (n=1024,p=536903681) in only 6.3 μs and 33.1 μs, respectively.
No takes yet. Share an insight, caveat, or question.
Chen et al. (2014) studied this question.
Synapse has enriched 10 related papers. Consider them for comparative context: