Energy/Area-Efficient Scalar Multiplication with Binary Edwards Curves for the IoT

Author:

Lara-Nino Carlos,Diaz-Perez Arturo,Morales-Sandoval Miguel

Abstract

Making Elliptic Curve Cryptography (ECC) available for the Internet of Things (IoT) and related technologies is a recent topic of interest. Modern IoT applications transfer sensitive information which needs to be protected. This is a difficult task due to the processing power and memory availability constraints of the physical devices. ECC mainly relies on scalar multiplication (kP)—which is an operation-intensive procedure. The broad majority of kP proposals in the literature focus on performance improvements and often overlook the energy footprint of the solution. Some IoT technologies—Wireless Sensor Networks (WSN) in particular—are critically sensitive in that regard. In this paper we explore energy-oriented improvements applied to a low-area scalar multiplication architecture for Binary Edwards Curves (BEC)—selected given their efficiency. The design and implementation costs for each of these energy-oriented techniques—in hardware—are reported. We propose an evaluation method for measuring the effectiveness of these optimizations. Under this novel approach, the energy-reducing techniques explored in this work contribute to achieving the scalar multiplication architecture with the most efficient area/energy trade-offs in the literature, to the best of our knowledge.

Funder

Consejo Nacional de Ciencia y Tecnología

Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

Cited by 9 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3