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.

Highlights

  • The deployment of Internet of Things (IoT) applications is pushing society to interact with smart environments on a regular basis

  • 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

  • We explore the area/energy tradeoffs on a FPGA-based realization of the multiplication scalar for Binary Edwards Curves (BECs) [23]

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Summary

Introduction

The deployment of Internet of Things (IoT) applications is pushing society to interact with smart environments on a regular basis. Smartphones, buildings, vehicles, roads, home appliances; most new instances of these technologies are being equipped with capabilities for data sensing and internet connectivity [1]. The data retrieved by these systems might be sensitive, since it can be inherently confidential [2] or can be used to infer a user’s behavior [3]. A common characteristic in many IoT nodes is that they suffer from physical constraints, most notably on size and energy [4,5]. Devices’ physical size needs to be decreased

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