Macquarie University
Browse
01whole.pdf (18.55 MB)

Efficient FPGA implementation of elliptic curve scalar multiplication over the binary field

Download (18.55 MB)
thesis
posted on 2022-03-28, 16:52 authored by Kuntapong Ruangsantikorakul
Elliptic curve cryptography (ECC) is the more efficient alternative to the widely used Ron Rivest, Adi Shamir and Leonard Adleman (RSA) cryptosystem since 283 bit ECC provides the same security per bit as 3072 bit RSA. This makes it useful for resource constrained portable devices since smaller operands can be used. For practical use of ECC, an efficient hardware implementation must be achieved in terms of area and time. Elliptic curve scalar multiplication (ECSM) is the crucial operation required for ECC processors. The goal of this research is the implementation of area efficient and high speed ECSM over a binary field in in field-programmable gate array (FGPA) technology.

History

Table of Contents

1. Introduction -- 2. Background and related work -- 3. Experimental procedures -- 4. Hardware for Galois field arithmetic -- 5. Hardware for ECSM -- 6. Optimised ECC operations -- 7. Results and discussion of ECC operations -- 8. Conclusions and future work -- 9. Abbreviations -- Appendices -- Bibliography.

Notes

Bibliography: pages 91-92 Empirical thesis.

Awarding Institution

Macquarie University

Degree Type

Thesis bachelor honours

Degree

BSc (Hons), Macquarie University, Faculty of Science and Engineering, School of Engineering

Department, Centre or School

School of Engineering

Year of Award

2016

Principal Supervisor

Yinan Kong

Additional Supervisor 1

Md Selim Hossain

Rights

Copyright Kuntapong Ruangsantikorakul 2016. Copyright disclaimer: http://mq.edu.au/library/copyright

Language

English

Extent

1 online resource (xviii, 92 pages diagrams, graphs, tables)

Former Identifiers

mq:70348 http://hdl.handle.net/1959.14/1262807

Usage metrics

    Macquarie University Theses

    Keywords

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC