JOURNAL ARTICLE
High-Performance Multi-RNS-Assisted Concurrent RSA Cryptosystem Architectures.
Published In: Journal of Circuits, Systems & Computers, 2023, v. 32, n. 15. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Elango, S.; Sampath, P.; Raja Sekar, S.; Philip, Sajan P; Danielraj, A. 3 of 3
Abstract
In public-key cryptography, the RSA algorithm is an inevitable part of hardware security because of the ease of implementation and security. RSA Cryptographic algorithm uses many modular arithmetic operations that decide the overall performance of the architecture. This paper proposes VLSI architecture to implement an RSA public-key cryptosystem driven by the Residue Number System (RNS). Modular exponentiation in the RSA algorithm is executed by dividing the entire process into modular squaring and multiplication operations. Based on the RNS employment in modulo-exponential operation, two RSA architectures are proposed. A Verilog HDL code is used to model the entire RSA architecture and ported in Zynq FPGA (XC7Z020CLG484-1) for Proof of Concept (PoC). The Cadence Genus Synthesizer tool characterizes a system's performance for TSMCs standard Cell library. Partial RNS (Proposed-I)- and Fully RNS (Proposed-II)-based RSA architectures increase the operation speed by 13% and 35%, respectively, compared with the existing RSA. Even though there is an increase in parameters like area, power and PDP for a smaller key size, the improvement in area utilization and encryption/ decryption speed of RSA for a larger key size is evident from the analysis. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Journal of Circuits, Systems & Computers. 2023/10, Vol. 32, Issue 15, p1
- Document Type:Article
- Subject Area:Computer Science
- Publication Date:2023
- ISSN:0218-1266
- DOI:10.1142/S0218126623502559
- Accession Number:172868051
- Copyright Statement:Copyright of Journal of Circuits, Systems & Computers is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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