International Association for Cryptologic Research

International Association
for Cryptologic Research

Transactions on Cryptographic Hardware and Embedded Systems, Volume 2023

Speeding Up Multi-Scalar Multiplication over Fixed Points Towards Efficient zkSNARKs


Guiwen Luo
University of Waterloo

Shihui Fu
University of Waterloo

Guang Gong
University of Waterloo


Keywords: Multi-scalar multiplication, Pippenger’s bucket method, zkSNARK, blockchain


Abstract

The arithmetic of computing multiple scalar multiplications in an elliptic curve group then adding them together is called multi-scalar multiplication (MSM). MSM over fixed points dominates the time consumption in the pairing-based trusted setup zero-knowledge succinct non-interactive argument of knowledge (zkSNARK), thus for practical applications we would appreciate fast algorithms to compute it. This paper proposes a bucket set construction that can be utilized in the context of Pippenger’s bucket method to speed up MSM over fixed points with the help of precomputation. If instantiating the proposed construction over BLS12-381 curve, when computing n-scalar multiplications for n = 2e (10 ≤ e ≤ 21), theoretical analysis ndicates that the proposed construction saves more than 21% computational cost compared to Pippenger’s bucket method, and that it saves 2.6% to 9.6% computational cost compared to the most popular variant of Pippenger’s bucket method. Finally, our experimental result demonstrates the feasibility of accelerating the computation of MSM over fixed points using large precomputation tables as well as the effectiveness of our new construction.

Publication

Transactions of Cryptographic Hardware and Embedded Systems, Volume 2023, Issue 2

Paper

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Artifact number
tches/2023/a7

Artifact published
June 21, 2024

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BibTeX How to cite

Luo, G., Fu, S., & Gong, G. (2023). Speeding Up Multi-Scalar Multiplication over Fixed Points Towards Efficient zkSNARKs. IACR Transactions on Cryptographic Hardware and Embedded Systems, 2023(2), 358–380. https://doi.org/10.46586/tches.v2023.i2.358-380. Artifact at https://artifacts.iacr.org/tches/2023/a7.