Parallel RSA encryption algorithm based on a ternary optical computer

被引:1
|
作者
Song, Kai [1 ]
Hu, Huanyu [1 ]
Yan, Liping [2 ]
Hou, Xinjun [1 ]
Wei, Jingyang [1 ]
机构
[1] East China Jiao Tong Univ, Sch Informat Engn, Shuanggang Rd, Nanchang 330013, Peoples R China
[2] East China Jiao Tong Univ, Software Sch, Shuanggang Rd, Nanchang 330013, Peoples R China
基金
中国国家自然科学基金;
关键词
IMPLEMENTATION; DESIGN;
D O I
10.1364/AO.536463
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
The RSA algorithm remains one of the most influential and extensively used public-key encryption algorithms in today's cryptographic domain. However, the traditional RSA encryption algorithm has several drawbacks, including slow computational speed, high computational cost, and substantial space requirements in practical applications. Consequently, this study amalgamates the characteristics of a ternary optical computer (TOC), including high parallelism, multi-digit precision, and low energy consumption, to research and propose a dual-channel transmission encryption strategy and a parallel rapid RSA encryption algorithm. Leveraging the dual-rotary optical structure of TOC and the simplest most significant digit (MSD) encoding, we implement a fundamental encryption strategy based on TOC. Concurrently, we harness the high-parallelism attributes of TOC to design large-number parallel modular exponentiation modules, enabling the generation of large prime numbers and efficient computation of large integer modular exponentiation operations. This innovation surmounts the drawbacks of traditional RSA algorithms and further enhances the efficiency of the encryption algorithm. Through resource and time analysis of the algorithm, as well as validation through experimental cases, it is evident that the TOC-based parallel rapid RSA encryption algorithm exhibits excellent computational efficiency and time performance. This research optimally exploits TOC's advantages in handling high parallelism and extensive computational tasks. (c) 2024 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.
引用
收藏
页码:6636 / 6645
页数:10
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