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The construction of self-dual normal polynomials over GF(2) and their applications to the Massey-Omura algorithm
Gaussian periods are used to locate a normal element of the finite field GF(2e) of odd degree e and an
algorithm is presented for the construction of self-dual normal polynomials over GF(2) for any odd degree.
This gives a new constructive proof of the existence of a self-dual basis for odd degree. The use of such
polynomials in the Massey-Omura multiplier improves the efficiency and decreases the complexity of the
multiplie
A generalized algorithm to design finite field normal basis multipliers
Finite field arithmetic logic is central in the implementation of some error-correcting coders and some cryptographic devices. There is a need for good multiplication algorithms which can be easily realized. Massey and Omura recently developed a new multiplication algorithm for finite fields based on a normal basis representation. Using the normal basis representation, the design of the finite field multiplier is simple and regular. The fundamental design of the Massey-Omura multiplier is based on a design of a product function. In this article, a generalized algorithm to locate a normal basis in a field is first presented. Using this normal basis, an algorithm to construct the product function is then developed. This design does not depend on particular characteristics of the generator polynomial of the field
Decoding Generalized Reed-Solomon Codes and Its Application to RLCE Encryption Schemes
This paper compares the efficiency of various algorithms for implementing
quantum resistant public key encryption scheme RLCE on 64-bit CPUs. By
optimizing various algorithms for polynomial and matrix operations over finite
fields, we obtained several interesting (or even surprising) results. For
example, it is well known (e.g., Moenck 1976 \cite{moenck1976practical}) that
Karatsuba's algorithm outperforms classical polynomial multiplication algorithm
from the degree 15 and above (practically, Karatsuba's algorithm only
outperforms classical polynomial multiplication algorithm from the degree 35
and above ). Our experiments show that 64-bit optimized Karatsuba's algorithm
will only outperform 64-bit optimized classical polynomial multiplication
algorithm for polynomials of degree 115 and above over finite field
. The second interesting (surprising) result shows that 64-bit
optimized Chien's search algorithm ourperforms all other 64-bit optimized
polynomial root finding algorithms such as BTA and FFT for polynomials of all
degrees over finite field . The third interesting (surprising)
result shows that 64-bit optimized Strassen matrix multiplication algorithm
only outperforms 64-bit optimized classical matrix multiplication algorithm for
matrices of dimension 750 and above over finite field . It should
be noted that existing literatures and practices recommend Strassen matrix
multiplication algorithm for matrices of dimension 40 and above. All our
experiments are done on a 64-bit MacBook Pro with i7 CPU and single thread C
codes. It should be noted that the reported results should be appliable to 64
or larger bits CPU architectures. For 32 or smaller bits CPUs, these results
may not be applicable. The source code and library for the algorithms covered
in this paper are available at http://quantumca.org/
Analysis of Parallel Montgomery Multiplication in CUDA
For a given level of security, elliptic curve cryptography (ECC) offers improved efficiency over classic public key implementations. Point multiplication is the most common operation in ECC and, consequently, any significant improvement in perfor- mance will likely require accelerating point multiplication. In ECC, the Montgomery algorithm is widely used for point multiplication. The primary purpose of this project is to implement and analyze a parallel implementation of the Montgomery algorithm as it is used in ECC. Specifically, the performance of CPU-based Montgomery multiplication and a GPU-based implementation in CUDA are compared
VLSI architectures for computing multiplications and inverses in GF(2-m)
Finite field arithmetic logic is central in the implementation of Reed-Solomon coders and in some cryptographic algorithms. There is a need for good multiplication and inversion algorithms that are easily realized on VLSI chips. Massey and Omura recently developed a new multiplication algorithm for Galois fields based on a normal basis representation. A pipeline structure is developed to realize the Massey-Omura multiplier in the finite field GF(2m). With the simple squaring property of the normal-basis representation used together with this multiplier, a pipeline architecture is also developed for computing inverse elements in GF(2m). The designs developed for the Massey-Omura multiplier and the computation of inverse elements are regular, simple, expandable and, therefore, naturally suitable for VLSI implementation
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