Improved Scalar Multiplication Based on ωNAFRepresentation of Feature Sequences Under Sliding Window Algorithm

Elliptic Curve Cryptography (ECC) is extensively utilized in various security applications due to its high efficiency and security. The core operation in ECC, elliptic curve scalar multiplication, significantly impacts the performance of the entire cryptographic system. To reduce the computational complexity of scalar multiplication, this paper proposes an improved algorithm that combines the sliding window method with the ωNAF (N on-Adjacent Form) algorithm, enhancing performance by representing ones feature sequences using the ωNAF algorithm. Theoretical analysis and experimental simulations demonstrate that this algorithm achieves lower complexity compared to the conventional sliding window algorithm with small windows.

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Published
2024-09-27
DOI
https://doi.org/10.1109/eiecs63941.2024.10799965
Primary Topic
Brain Tumor Detection and Classification
Type
article
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article

Improved Scalar Multiplication Based on ωNAFRepresentation of Feature Sequences Under Sliding Window Algorithm

Le Wang, Junhao Li
Brain Tumor Detection and Classification
article

Improved Scalar Multiplication Based on ωNAFRepresentation of Feature Sequences Under Sliding Window Algorithm

Le Wang, Junhao Li
article en

Abstract

Elliptic Curve Cryptography (ECC) is extensively utilized in various security applications due to its high efficiency and security. The core operation in ECC, elliptic curve scalar multiplication, significantly impacts the performance of the entire cryptographic system. To reduce the computational complexity of scalar multiplication, this paper proposes an improved algorithm that combines the sliding window method with the ωNAF (N on-Adjacent Form) algorithm, enhancing performance by representing ones feature sequences using the ωNAF algorithm. Theoretical analysis and experimental simulations demonstrate that this algorithm achieves lower complexity compared to the conventional sliding window algorithm with small windows.

North China University of Technology (CN)
Openalex Percentile: Top 73%
Brain Tumor Detection and Classification
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