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Problems in Abstract Algebra
STUDENT MATHEMATICAL LIBRARY Volume 82 Problems in Abstract Algebra A. R. Wadsworth 10.1090/stml/082 STUDENT MATHEMATICAL LIBRARY Volume 82 Problems in Abstract Algebra A. R. Wadsworth American Mathematical Society Providence, Rhode Island Editorial Board Satyan L. Devadoss John Stillwell (Chair) Erica Flapan Serge Tabachnikov 2010 Mathematics Subject Classification. Primary 00A07, 12-01, 13-01, 15-01, 20-01. For additional information and updates on this book, visit www.ams.org/bookpages/stml-82 Library of Congress Cataloging-in-Publication Data Names: Wadsworth, Adrian R., 1947– Title: Problems in abstract algebra / A. R. Wadsworth. Description: Providence, Rhode Island: American Mathematical Society, [2017] | Series: Student mathematical library; volume 82 | Includes bibliographical references and index. Identifiers: LCCN 2016057500 | ISBN 9781470435837 (alk. paper) Subjects: LCSH: Algebra, Abstract – Textbooks. | AMS: General – General and miscellaneous specific topics – Problem books. msc | Field theory and polyno- mials – Instructional exposition (textbooks, tutorial papers, etc.). msc | Com- mutative algebra – Instructional exposition (textbooks, tutorial papers, etc.). msc | Linear and multilinear algebra; matrix theory – Instructional exposition (textbooks, tutorial papers, etc.). msc | Group theory and generalizations – Instructional exposition (textbooks, tutorial papers, etc.). msc Classification: LCC QA162 .W33 2017 | DDC 512/.02–dc23 LC record available at https://lccn.loc.gov/2016057500 Copying and reprinting. Individual readers of this publication, and nonprofit libraries acting for them, are permitted to make fair use of the material, such as to copy select pages for use in teaching or research. Permission is granted to quote brief passages from this publication in reviews, provided the customary acknowledgment of the source is given. Republication, systematic copying, or multiple reproduction of any material in this publication is permitted only under license from the American Mathematical Society. -
VHDL Implementation of Twofish Algorithm
VHDL Implementation of Twofish Algorithm Purnima Gehlot Richa Sharma S. R. Biradar Mody Institute of Mody Institute of Mody Institute of Technology Technology Technology and Science and Science and Science Laxmangarh, Sikar Laxmangarh, Sikar Laxmangarh, Sikar Rajasthan,India Rajasthan,India Rajasthan,India [email protected] [email protected] [email protected] which consists of two modules of function g, one MDS i.e. maximum ABSTRACT distance separable matrix,a PHT i.e. pseudo hadamard transform and Every day hundreds and thousands of people interact electronically, two adders of 32-bit for one round. To increase the complexity of the whether it is through emails, e-commerce, etc. through internet. For algorithm we can perform Endian function over the input bit stream. sending sensitive messages over the internet, we need security. In this Different modules of twofish algorithms are: paper a security algorithms, Twofish (Symmetric key cryptographic algorithm) [1] has been explained. All the important modules of 2.1 ENDIAN FUNCTION Twofish algorithm, which are Function F and g, MDS, PHT, are implemented on Xilinx – 6.1 xst software and there delay calculations Endian Function is a transformation of the input data. It is used as an interface between the input data provided to the circuit and the rest has been done on FPGA families which are Spartan2, Spartan2E and of the cipher. It can be used with all the key-sizes [5]. Here 128-bit VirtexE. input is divided into 16 bytes from byte0 to byte15 and are rearranged to get the output of 128-bit. Keywords Twofish, MDS, PHT, symmetric key, Function F and g. -
The Whirlpool Secure Hash Function
Cryptologia, 30:55–67, 2006 Copyright Taylor & Francis Group, LLC ISSN: 0161-1194 print DOI: 10.1080/01611190500380090 The Whirlpool Secure Hash Function WILLIAM STALLINGS Abstract In this paper, we describe Whirlpool, which is a block-cipher-based secure hash function. Whirlpool produces a hash code of 512 bits for an input message of maximum length less than 2256 bits. The underlying block cipher, based on the Advanced Encryption Standard (AES), takes a 512-bit key and oper- ates on 512-bit blocks of plaintext. Whirlpool has been endorsed by NESSIE (New European Schemes for Signatures, Integrity, and Encryption), which is a European Union-sponsored effort to put forward a portfolio of strong crypto- graphic primitives of various types. Keywords advanced encryption standard, block cipher, hash function, sym- metric cipher, Whirlpool Introduction In this paper, we examine the hash function Whirlpool [1]. Whirlpool was developed by Vincent Rijmen, a Belgian who is co-inventor of Rijndael, adopted as the Advanced Encryption Standard (AES); and by Paulo Barreto, a Brazilian crypto- grapher. Whirlpool is one of only two hash functions endorsed by NESSIE (New European Schemes for Signatures, Integrity, and Encryption) [13].1 The NESSIE project is a European Union-sponsored effort to put forward a portfolio of strong cryptographic primitives of various types, including block ciphers, symmetric ciphers, hash functions, and message authentication codes. Background An essential element of most digital signature and message authentication schemes is a hash function. A hash function accepts a variable-size message M as input and pro- duces a fixed-size hash code HðMÞ, sometimes called a message digest, as output. -
Lightweight Diffusion Layer from the Kth Root of the MDS Matrix
Lightweight Diffusion Layer from the kth root of the MDS Matrix Souvik Kolay1, Debdeep Mukhopadhyay1 1Dept. of Computer Science and Engineering Indian Institute of Technology Kharagpur, India fsouvik1809,[email protected] Abstract. The Maximum Distance Separable (MDS) mapping, used in cryptog- raphy deploys complex Galois field multiplications, which consume lots of area in hardware, making it a costly primitive for lightweight cryptography. Recently in lightweight hash function: PHOTON, a matrix denoted as ‘Serial’, which re- quired less area for multiplication, has been multiplied 4 times to achieve a lightweight MDS mapping. But no efficient method has been proposed so far to synthesize such a serial matrix or to find the required number of repetitive multiplications needed to be performed for a given MDS mapping. In this pa- per, first we provide an generic algorithm to find out a low-cost matrix, which can be multiplied k times to obtain a given MDS mapping. Further, we optimize the algorithm for using in cryptography and show an explicit case study on the MDS mapping of the hash function PHOTON to obtain the ‘Serial’. The work also presents quite a few results which may be interesting for lightweight imple- mentation. Keywords: MDS Matrix, kth Root of a Matrix, Lightweight Diffusion Layer 1 Introduction With several resource constrained devices requiring support for cryptographic algo- rithms, the need for lightweight cryptography is imperative. Several block ciphers, like Kasumi[19], mCrypton[24], HIGHT[20], DESL and DESXL[23], CLEFIA[34], Present[8], Puffin[11], MIBS[22], KATAN[9], Klein[14], TWINE[36], LED[16], Pic- colo [33] etc and hash functions like PHOTON[33], SPONGENT[7], Quark [3] etc. -
Circulant and Toeplitz Matrices in Compressed Sensing
Circulant and Toeplitz Matrices in Compressed Sensing Holger Rauhut Hausdorff Center for Mathematics & Institute for Numerical Simulation University of Bonn Conference on Time-Frequency Strobl, June 15, 2009 Institute for Numerical Simulation Rheinische Friedrich-Wilhelms-Universität Bonn Holger Rauhut Hausdorff Center for Mathematics & Institute for NumericalCirculant and Simulation Toeplitz University Matrices inof CompressedBonn Sensing Overview • Compressive Sensing (compressive sampling) • Partial Random Circulant and Toeplitz Matrices • Recovery result for `1-minimization • Numerical experiments • Proof sketch (Non-commutative Khintchine inequality) Holger Rauhut Circulant and Toeplitz Matrices 2 Recovery requires the solution of the underdetermined linear system Ax = y: Idea: Sparsity allows recovery of the correct x. Suitable matrices A allow the use of efficient algorithms. Compressive Sensing N Recover a large vector x 2 C from a small number of linear n×N measurements y = Ax with A 2 C a suitable measurement matrix. Usual assumption x is sparse, that is, kxk0 := j supp xj ≤ k where supp x = fj; xj 6= 0g. Interesting case k < n << N. Holger Rauhut Circulant and Toeplitz Matrices 3 Compressive Sensing N Recover a large vector x 2 C from a small number of linear n×N measurements y = Ax with A 2 C a suitable measurement matrix. Usual assumption x is sparse, that is, kxk0 := j supp xj ≤ k where supp x = fj; xj 6= 0g. Interesting case k < n << N. Recovery requires the solution of the underdetermined linear system Ax = y: Idea: Sparsity allows recovery of the correct x. Suitable matrices A allow the use of efficient algorithms. Holger Rauhut Circulant and Toeplitz Matrices 3 n×N For suitable matrices A 2 C , `0 recovers every k-sparse x provided n ≥ 2k. -
Analytical Solution of the Symmetric Circulant Tridiagonal Linear System
Rose-Hulman Institute of Technology Rose-Hulman Scholar Mathematical Sciences Technical Reports (MSTR) Mathematics 8-24-2014 Analytical Solution of the Symmetric Circulant Tridiagonal Linear System Sean A. Broughton Rose-Hulman Institute of Technology, [email protected] Jeffery J. Leader Rose-Hulman Institute of Technology, [email protected] Follow this and additional works at: https://scholar.rose-hulman.edu/math_mstr Part of the Numerical Analysis and Computation Commons Recommended Citation Broughton, Sean A. and Leader, Jeffery J., "Analytical Solution of the Symmetric Circulant Tridiagonal Linear System" (2014). Mathematical Sciences Technical Reports (MSTR). 103. https://scholar.rose-hulman.edu/math_mstr/103 This Article is brought to you for free and open access by the Mathematics at Rose-Hulman Scholar. It has been accepted for inclusion in Mathematical Sciences Technical Reports (MSTR) by an authorized administrator of Rose-Hulman Scholar. For more information, please contact [email protected]. Analytical Solution of the Symmetric Circulant Tridiagonal Linear System S. Allen Broughton and Jeffery J. Leader Mathematical Sciences Technical Report Series MSTR 14-02 August 24, 2014 Department of Mathematics Rose-Hulman Institute of Technology http://www.rose-hulman.edu/math.aspx Fax (812)-877-8333 Phone (812)-877-8193 Analytical Solution of the Symmetric Circulant Tridiagonal Linear System S. Allen Broughton Rose-Hulman Institute of Technology Jeffery J. Leader Rose-Hulman Institute of Technology August 24, 2014 Abstract A circulant tridiagonal system is a special type of Toeplitz system that appears in a variety of problems in scientific computation. In this paper we give a formula for the inverse of a symmetric circulant tridiagonal matrix as a product of a circulant matrix and its transpose, and discuss the utility of this approach for solving the associated system. -
Discovering Transforms: a Tutorial on Circulant Matrices, Circular Convolution, and the Discrete Fourier Transform
DISCOVERING TRANSFORMS: A TUTORIAL ON CIRCULANT MATRICES, CIRCULAR CONVOLUTION, AND THE DISCRETE FOURIER TRANSFORM BASSAM BAMIEH∗ Key words. Discrete Fourier Transform, Circulant Matrix, Circular Convolution, Simultaneous Diagonalization of Matrices, Group Representations AMS subject classifications. 42-01,15-01, 42A85, 15A18, 15A27 Abstract. How could the Fourier and other transforms be naturally discovered if one didn't know how to postulate them? In the case of the Discrete Fourier Transform (DFT), we show how it arises naturally out of analysis of circulant matrices. In particular, the DFT can be derived as the change of basis that simultaneously diagonalizes all circulant matrices. In this way, the DFT arises naturally from a linear algebra question about a set of matrices. Rather than thinking of the DFT as a signal transform, it is more natural to think of it as a single change of basis that renders an entire set of mutually-commuting matrices into simple, diagonal forms. The DFT can then be \discovered" by solving the eigenvalue/eigenvector problem for a special element in that set. A brief outline is given of how this line of thinking can be generalized to families of linear operators, leading to the discovery of the other common Fourier-type transforms, as well as its connections with group representations theory. 1. Introduction. The Fourier transform in all its forms is ubiquitous. Its many useful properties are introduced early on in Mathematics, Science and Engineering curricula [1]. Typically, it is introduced as a transformation on functions or signals, and then its many useful properties are easily derived. Those properties are then shown to be remarkably effective in solving certain differential equations, or in an- alyzing the action of time-invariant linear dynamical systems, amongst many other uses. -
Variations to the Cryptographic Algorithms Aes and Twofish
VARIATIONS TO THE CRYPTOGRAPHIC ALGORITHMS AES AND TWOFISH P. Freyre∗1, N. D´ıaz ∗ and O. Cuellar y2 ∗Faculty of Mathematics and Computer Science, University of Havana, Cuba. yFaculty of Mathematics, Physical and Computer Science, Central University of Las Villas, Cuba. 1e-mail: [email protected] 2e-mail: [email protected] Abstract The cryptographic algorithms AES and Twofish guarantee a high diffusion by the use of fixed 4×4 MDS matrices. In this article variations to the algorithms AES and Twofish are made. They allow that the process of cipher - decipher come true with MDS matrices selected randomly from the set of all MDS matrices with the use of proposed algorithm. A new key schedule with a high diffusion is designed for the algorithm AES. Besides proposed algorithm generate new S - box that depends of the key. Key words: Block Cipher, AES, Twofish, S - boxes, MDS matrix. 1 1 Introduction. The cryptographic algorithms Rijndael (see [DR99] and [DR02]) and Twofish (see [SKWHF98] and [GBS13]) were finalists of the contest to select the Advanced Encryption Standard (AES) convened by the National Institute of Standards and Technology from the United States (NIST). The contest finished in October 2000 with the selection of the algorithm Rijndael as the AES, stated algorithm was proposed by Joan Daemen and Vincent Rijmen from Belgium. In order to reach a high diffusion, the algorithms AES and Twofish, use a MDS (Maximal Distance Separable) matrices, selected a priori. In this article we will explain the variations of these algorithms, where the MDS matrices are selected randomly as function of the secret key. -
Linear Algebra: Example Sheet 3 of 4
Michaelmas Term 2020 Linear Algebra: Example Sheet 3 of 4 1. Find the eigenvalues and give bases for the eigenspaces of the following complex matrices: 0 1 1 0 1 0 1 1 −1 1 0 1 1 −1 1 @ 0 3 −2 A ; @ 0 3 −2 A ; @ −1 3 −1 A : 0 1 0 0 1 0 −1 1 1 The second and third matrices commute; find a basis with respect to which they are both diagonal. 2. By considering the rank or minimal polynomial of a suitable matrix, find the eigenvalues of the n × n matrix A with each diagonal entry equal to λ and all other entries 1. Hence write down the determinant of A. 3. Let A be an n × n matrix all the entries of which are real. Show that the minimum polynomial of A, over the complex numbers, has real coefficients. 4. (i) Let V be a vector space, let π1; π2; : : : ; πk be endomorphisms of V such that idV = π1 + ··· + πk and πiπj = 0 for any i 6= j. Show that V = U1 ⊕ · · · ⊕ Uk, where Uj = Im(πj). (ii) Let α be an endomorphism of V satisfying the equation α3 = α. By finding suitable endomorphisms of V depending on α, use (i) to prove that V = V0 ⊕ V1 ⊕ V−1, where Vλ is the λ-eigenspace of α. (iii) Apply (i) to prove the Generalised Eigenspace Decomposition Theorem stated in lectures. [Hint: It may help to re-read the proof of the Diagonalisability Theorem given in lectures. You may use the fact that if p1(t); : : : ; pk(t) 2 C[t] are non-zero polynomials with no common factor, Euclid's algorithm implies that there exist polynomials q1(t); : : : ; qk(t) 2 C[t] such that p1(t)q1(t) + ::: + pk(t)qk(t) = 1.] 5. -
On Constructions of MDS Matrices from Circulant-Like Matrices for Lightweight Cryptography
On Constructions of MDS Matrices From Circulant-Like Matrices For Lightweight Cryptography Technical Report No. ASU/2014/1 Dated : 14th February, 2014 Kishan Chand Gupta Applied Statistics Unit Indian Statistical Institute 203, B. T. Road, Kolkata 700108, INDIA. [email protected] Indranil Ghosh Ray Applied Statistics Unit Indian Statistical Institute 203, B. T. Road, Kolkata 700108, INDIA. indranil [email protected] On Constructions of MDS Matrices From Circulant-Like Matrices For Lightweight Cryptography Kishan Chand Gupta and Indranil Ghosh Ray Applied Statistics Unit, Indian Statistical Institute. 203, B. T. Road, Kolkata 700108, INDIA. [email protected], indranil [email protected] Abstract. Maximum distance separable (MDS) matrices have applications not only in coding theory but are also of great importance in the design of block ciphers and hash functions. It is highly nontrivial to find MDS matrices which could be used in lightweight cryptography. In a SAC 2004 paper, Junod et. al. constructed a new class of efficient MDS matrices whose submatrices were circulant matrices and they coined the term circulating-like matrices for these new class of matrices which we rename as circulant-like matrices. In this paper we study this construction and propose efficient 4 × 4 and 8 × 8 circulant-like MDS matrices. We prove that such d × d circulant-like MDS matrices can not be involutory or orthogonal which are good for designing SPN networks. Although these matrices are efficient, but the inverse of such matrices are not guaranteed to be efficient. Towards this we design a new type of circulant- like MDS matrices which are by construction involutory. -
On Constructions of MDS Matrices from Companion Matrices for Lightweight Cryptography
On Constructions of MDS Matrices from Companion Matrices for Lightweight Cryptography Kishan Chand Gupta and Indranil Ghosh Ray Applied Statistics Unit, Indian Statistical Institute. 203, B. T. Road, Kolkata 700108, INDIA. [email protected], indranil [email protected] Abstract. Maximum distance separable (MDS) matrices have applications not only in coding theory but also are of great importance in the design of block ciphers and hash functions. It is highly nontrivial to find MDS matrices which could be used in lightweight cryptography. In a crypto 4 2011 paper, Guo et. al. proposed a new MDS matrix Serial(1; 2; 1; 4) over F28 . This representation has a compact hardware implementation of the AES MixColumn operation. No general study of d MDS properties of this newly introduced construction of the form Serial(z0; : : : ; zd−1) over F2n for arbitrary d and n is available in the literature. In this paper we study some properties of d MDS matrices and provide an insight of why Serial(z0; : : : ; zd−1) leads to an MDS matrix. For 2 efficient hardware implementation, we aim to restrict the values of zi's in f1; α; α ; α+1g, such that d Serial(z0; : : : ; zd−1) is MDS for d = 4 and 5, where α is the root of the constructing polynomial of F2n . We also propose more generic constructions of MDS matrices e.g. we construct lightweight 4 × 4 and 5 × 5 MDS matrices over F2n for all n ≥ 4. An algorithm is presented to check if a given matrix is MDS. The algorithm directly follows from the basic properties of MDS matrix and is easy to implement. -
Factoring Matrices Into the Product of Circulant and Diagonal Matrices
FACTORING MATRICES INTO THE PRODUCT OF CIRCULANT AND DIAGONAL MATRICES MARKO HUHTANEN∗ AND ALLAN PERAM¨ AKI¨ y n×n Abstract. A generic matrix A 2 C is shown to be the product of circulant and diagonal matrices with the number of factors being 2n−1 at most. The demonstration is constructive, relying on first factoring matrix subspaces equivalent to polynomials in a permutation matrix over diagonal matrices into linear factors. For the linear factors, the sum of two scaled permutations is factored into the product of a circulant matrix and two diagonal matrices. Extending the monomial group, both low degree and sparse polynomials in a permutation matrix over diagonal matrices, together with their permutation equivalences, constitute a fundamental sparse matrix structure. Matrix analysis gets largely done polynomially, in terms of permutations only. Key words. circulant matrix, diagonal matrix, DFT, Fourier optics, sparsity structure, matrix factoring, polynomial factoring, multiplicative Fourier compression AMS subject classifications. 15A23, 65T50, 65F50, 12D05 1. Introduction. There exists an elegant result, motivated by applications in optical image processing, stating that any matrix A 2 Cn×n is the product of circu- lant and diagonal matrices [15, 18].1 In this paper it is shown that, generically, 2n − 1 factors suffice. (For various aspects of matrix factoring, see [13].) The demonstration is constructive, relying on first factoring matrix subspaces equivalent to polynomials in a permutation matrix over diagonal matrices into linear factors. This is achieved by solving structured systems of polynomial equations. Located on the borderline between commutative and noncommutative algebra, such subspaces are shown to constitute a fundamental sparse matrix structure of polynomial type extending, e.g., band matrices.