7. Modern Techniques. Data Encryption Standard (DES)
|
|
- Tamsyn Wilkins
- 5 years ago
- Views:
Transcription
1 7. Moder Techiques. Data Ecryptio Stadard (DES) The objective of this chapter is to illustrate the priciples of moder covetioal ecryptio. For this purpose, we focus o the most widely used covetioal ecryptio algorithms: the Data Ecryptio Stadard (DES). Although umerous covetioal ecryptio algorithms have bee developed sice the itroductio of DES, it remais the most importat algorithm. Further, a detailed study of DES provides a uderstadig of the priciples used i other covetioal ecryptio algorithms. Compared to public-key ecryptio schemes such as RSA, the structure of DES, ad most covetioal ecryptio algorithms, is very complex ad caot be explaied as easily as RSA ad similar algorithms. Accordigly, we begi with a simplified versio of DES. This versio allows the reader to perform ecryptio ad decryptio by had ad gai a good uderstadig of the workig of the algorithm details. Classroom experiece idicates that a study of this simplified versio ehaces uderstadig of DES. Simplified DES Simplified DES is a educatioal rather tha a secure ecryptio algorithm. It has similar properties ad structure to DES with much smaller parameters. It was developed by rofessor Edward Schaefer of Sata Clara Uiversity. Overview Figure 7.. illustrates the overall structure of the simplified DES, which we will refer to as S- DES. The S-DES ecryptio algorithm takes a -bit block of plaitext ad a 0-bit key as iput ad produces a -bit block of ciphertext as output. The S-DES decryptio algorithm takes a -bit block of ciphertext ad the same 0-bit key used to produce that ciphertext as iput ad produces the origial -bit block of plaitext. The ecryptio algorithm ivolves five fuctios: a iitial permutatio (I); a complex fuctio labeled f k, which ivolves both permutatio ad substitutio operatios ad depeds o a key iput; a simple permutatio fuctio that switches (SW) the two halves of the data; the fuctio f k agai, ad fially a permutatio fuctio that is iverse of the iitial permutatio (I - ). As was metioed, the use of multiple stages of permutatio ad substitutio results i a more complex algorithm, which icreases the difficulty of cryptaalysis. The fuctio f k takes as iput ot oly the data passig through the ecryptio algorithm, but also a -bit key. The algorithm could have bee desiged to work with a 6- bit key, cosistig of two -bit subkeys, oe used for each occurrece of f k. alteratively, a sigle -bit key could have bee used, with the same key used twice i the algorithm. A compromise is to use a 0-bit key from which two -bit subkeys are geerated, as depicted. I this case, the key is first subjected to a permutatio (0). The a shift operatio is performed. The output of the shift operatio the passes through a permutatio fuctio that produces a -bit output () for the first subkey (K ). The output of the shift operatio also feeds ito aother shift ad aother istace of to produce the secod subkey (K ). 9
2 Ecryptio -bit plaitext Key Geeratio 0-bit key 0 Shift Decryptio -bit plaitext I f k SW f k K K Shift K K I - f k SW f k I - I -bit ciphertext Fig. 7. -bit ciphertext We ca cocisely the ecryptio algorithm as a compositio of fuctios: I - f K SW f K I Which ca also be writte as Ciphertext = I - (f K (SW(f K (I(plaitext))))) Where K = (Shift (0(key))) K = (Shift(Shift (0(key)))) Decryptio is also show ad is essetially the reverse of ecryptio: laitext = I (f K (SW(f K (I - (ciphertext))))) We ow examie the elemets of S-DES i more detail. 0
3 0-bit key LS- 0 LS- K LS- LS- K S-DES key Geeratio Fig.7. S-DES depeds o the use of a 0-bit key shared betwee seder ad receiver. From this key, two -bit subkeys are produced for use i particular stages of the ecryptio ad decryptio algorithm. Depicts the stages followed to produce the subkeys. First, permute the key i the followig fashio. Let the 0-bit key be desigated as (k, k, k, k, k, k 6, k 7, k, k 9, k 0 ). The the permutatio 0 is defied as 0 (k, k, k, k, k, k 6, k 7, k, k 9, k 0 ) = (k, k, k, k 7, k, k 0, k, k 9, k, k 6 ) 0 ca be cocisely defied by the display: This table is read from left to right; each positio i the table gives the idetity of the iput bit that produces the output bit i that positio. So the first output bit is bit of the iput; the secod output bit is bit of the iput, ad so o. Next, perform a circular left shift (LS-), or rotatio, separately o the fist five bits ad the secod five bits. Next we apply, which picks out ad permutes of the 0 bits accordig to the followig rule: The result is subkey (K ). We tha go back to the pair of -bits strig produced by the two LS- fuctio ad perform a circular left sift of bit positios o each strig. Fially, is applied agai to produce K.
4 S-DES Ecryptio The S-DES ecryptio algorithm i greater detail. As was metioed, ecryptio ivolves the sequetial applicatio of five fuctios. We examie each of these. -bit laitext I E/ fk F S K SW E/ fk F K S I - -bit Ciphertext Fig. 7. Ecryptio Iitial ad Fial ermutatios The iput to the algorithm is a -bit block of plaitext, which we first permute usig the I fuctio:
5 I 6 7 This retais all bits of the plaitext but mixes them up. At the ed of the algorithm, the iverse permutatio is used. I It is easy to show by example that the secod permutatio is ideed the reverse of the first; that is, I - (I(X)) = X. The Fuctio f k The most complex compoet of S-DES is the fuctio f k, which cosists of a combiatio of permutatio ad substitutio fuctios. The fuctios ca be expressed as follows. Let L ad R be the leftmost bits ad rightmost bits of the -bit iput to f k, ad let F be a mappig (ot ecessarily oe-to-oe) from -bit strigs to -bit strigs. The we let f k (L, R) = ( L F( R, SK), R) where SK is a subkey ad is the bit-by-bit exclusive- OR fuctio. For example, suppose the output of the I stage is (00) ad F(0, SK) = (0) for some key SK. The f k (00) = (000) because (0) (0) = (00). We ow describe the mappig F. The iput is a -bit umber ( ). The first operatio is a expasio/ permutatio operatio: E/ For what follows, it is clearer to depict the result i this fashio: The -bit subkey K = (k, k, k, k, k, k 6, k 7, k ) is added to this value usig exclusive- OR: k k k k 6 k k 7 k k Let us reame these bits: 0,0,0 0,, 0,, 0,,
6 This first four bits (first row of the precedig matrix) are fed ito the S-box to produce a - bit output, ad the remaiig bits (secod row) are fed ito S to produce aother -bit output. These two boxes are defied as follows: S The S-boxes operate as follows: the first ad fourth iput bits are treated as -bit umbers that specify a row of the S-box, ad the secod ad third iput bits specify a colum of the S-box. The etry i that row ad colum, i base, is the -bit output. For example, if ( 0,0 0, ) = (00) ad ( 0, 0, ) = (0), the the output is from row0, colum of, which is, or () i biary. Similarly, (,0, ) ad (,, ) are used to idex ito a row ad colum of S to produce a additioal bits. Next, the bits produced by ad S udergo a further permutatio as follows: The output of is the output of the fuctio F. The Switch Fuctio The fuctio f k oly alters the leftmost bits of the iput. The switch fuctio (SW) iterchages the left ad right bits so that the secod istace of f k operates o a differet bits. I this secod istace, the E/,, S, ad fuctios are the same. The key iput is K. Example: S-DES Ecryptio Suppose: = 000 K= bit key shared betwee seder ad receiver. Two -bit sub keys are produced for use i particular stages. First, Let the 0-bit key be desigated as (k, k, k, k, k, k 6, k 7, k, k 9, k 0 ) the the permutatio 0 is defied as 0 (k, k, k, k, k, k 6, k 7, k, k 9, k 0 ) = (k, k, k, k 7, k, k 0, k, k 9, k, k 6 ) a) Key geeratio We start by permutig the key accordig to the permutatio 0 :
7 The result we obtai is 0000 We split the result i two groups of symbols (leftmost ad rightmost): 000 ad bit key LS- LS K= LS- LS K= Fig. 7. From this poit, we used the data to obtai the two itermediate key: -for K we group the bits together agai: 0000 ad perform oe last permutatio that also reduces the umber of bits to. For this we have: =( ) The result is: K =(00) -to obtai K we take the two groups ad shift them twice to the left: 000 ad 0 We group the bits together ad perform agai obtaiig the fial result: K =(000) b) Ecryptio of the plaitext: Start with =000 ad perform the iitial permutatio give by: I = ( 6 7)
8 The result we obtaied, 000 -bit laitext I E/ 00 fk F S 00 0 K SW fk F E/ K 000 S I bit Ciphertext Fig. 7. The first f k : Work o the last bits (00) Apply E/ (E/ = ( )) ad obtai
9 Compute exclusive or with the key K (remember, we obtaied as 00). The result is: 0000 Take the four leftmost bits-0 ad use them with S 0 ad the four rightmost bits-0 ad use them with S. From 0 we use st ad rd bits() for select colum ad d ad th bits (0) to select a row. Itersectio will be 00. From 0 we use st ad rd bits(0) for select colum ad d ad th bits () to select a row Itersectio will be S Apply 000 to the permutatio to it ( = ( )): 000 erform XOR betwee these bits ad the first bits obtaied after the I: = 00 At the ed of the first f k we have: 0000 Switch the two bits sequeces, we get: 0000 The secod f k: Work o the last bits (00): Apply E/ to the four bits ad obtai: 0000 Compute exclusive or with K (000). This takes us to: We are ready for the S-boxes. Use the leftmost bits with S 0 ad the rightmost bits with S. We get: 7
10 ad i.e. 0 i biary. Applyig to it we get 0 Fially, we do exclusive or: 0 00 = 000 The result of the secod f k is: To obtai the ciphertext we apply I - = ( 7 6) Ad we get: S-DES Decryptio C = K = 0000 a) Key geeratio NOTE: The key geeratio process is idetical betwee ecryptio ad decryptio. Sice we use the same keys. K = 00; K = 000 b) Decryptio of the ciphertext: The decryptio process is the reverse of the ecryptio i the sese that we start by permutig the ciphertext ad the we pass it through the two f k boxes.
A Block Cipher Using Linear Congruences
Joural of Computer Sciece 3 (7): 556-560, 2007 ISSN 1549-3636 2007 Sciece Publicatios A Block Cipher Usig Liear Cogrueces 1 V.U.K. Sastry ad 2 V. Jaaki 1 Academic Affairs, Sreeidhi Istitute of Sciece &
More informationCHAPTER I: Vector Spaces
CHAPTER I: Vector Spaces Sectio 1: Itroductio ad Examples This first chapter is largely a review of topics you probably saw i your liear algebra course. So why cover it? (1) Not everyoe remembers everythig
More informationTHE ASYMPTOTIC COMPLEXITY OF MATRIX REDUCTION OVER FINITE FIELDS
THE ASYMPTOTIC COMPLEXITY OF MATRIX REDUCTION OVER FINITE FIELDS DEMETRES CHRISTOFIDES Abstract. Cosider a ivertible matrix over some field. The Gauss-Jorda elimiatio reduces this matrix to the idetity
More informationLecture 11: Pseudorandom functions
COM S 6830 Cryptography Oct 1, 2009 Istructor: Rafael Pass 1 Recap Lecture 11: Pseudoradom fuctios Scribe: Stefao Ermo Defiitio 1 (Ge, Ec, Dec) is a sigle message secure ecryptio scheme if for all uppt
More informationSequences A sequence of numbers is a function whose domain is the positive integers. We can see that the sequence
Sequeces A sequece of umbers is a fuctio whose domai is the positive itegers. We ca see that the sequece 1, 1, 2, 2, 3, 3,... is a fuctio from the positive itegers whe we write the first sequece elemet
More informationEnd-of-Year Contest. ERHS Math Club. May 5, 2009
Ed-of-Year Cotest ERHS Math Club May 5, 009 Problem 1: There are 9 cois. Oe is fake ad weighs a little less tha the others. Fid the fake coi by weighigs. Solutio: Separate the 9 cois ito 3 groups (A, B,
More informationChapter 7 COMBINATIONS AND PERMUTATIONS. where we have the specific formula for the binomial coefficients:
Chapter 7 COMBINATIONS AND PERMUTATIONS We have see i the previous chapter that (a + b) ca be writte as 0 a % a & b%þ% a & b %þ% b where we have the specific formula for the biomial coefficiets: '!!(&)!
More informationInverse Matrix. A meaning that matrix B is an inverse of matrix A.
Iverse Matrix Two square matrices A ad B of dimesios are called iverses to oe aother if the followig holds, AB BA I (11) The otio is dual but we ofte write 1 B A meaig that matrix B is a iverse of matrix
More informationCommutativity in Permutation Groups
Commutativity i Permutatio Groups Richard Wito, PhD Abstract I the group Sym(S) of permutatios o a oempty set S, fixed poits ad trasiet poits are defied Prelimiary results o fixed ad trasiet poits are
More informationA sequence of numbers is a function whose domain is the positive integers. We can see that the sequence
Sequeces A sequece of umbers is a fuctio whose domai is the positive itegers. We ca see that the sequece,, 2, 2, 3, 3,... is a fuctio from the positive itegers whe we write the first sequece elemet as
More informationSolutions to Final Exam
Solutios to Fial Exam 1. Three married couples are seated together at the couter at Moty s Blue Plate Dier, occupyig six cosecutive seats. How may arragemets are there with o wife sittig ext to her ow
More informationDiscrete-Time Systems, LTI Systems, and Discrete-Time Convolution
EEL5: Discrete-Time Sigals ad Systems. Itroductio I this set of otes, we begi our mathematical treatmet of discrete-time s. As show i Figure, a discrete-time operates or trasforms some iput sequece x [
More informationMath 475, Problem Set #12: Answers
Math 475, Problem Set #12: Aswers A. Chapter 8, problem 12, parts (b) ad (d). (b) S # (, 2) = 2 2, sice, from amog the 2 ways of puttig elemets ito 2 distiguishable boxes, exactly 2 of them result i oe
More informationSection 5.1 The Basics of Counting
1 Sectio 5.1 The Basics of Coutig Combiatorics, the study of arragemets of objects, is a importat part of discrete mathematics. I this chapter, we will lear basic techiques of coutig which has a lot of
More informationa for a 1 1 matrix. a b a b 2 2 matrix: We define det ad bc 3 3 matrix: We define a a a a a a a a a a a a a a a a a a
Math S-b Lecture # Notes This wee is all about determiats We ll discuss how to defie them, how to calculate them, lear the allimportat property ow as multiliearity, ad show that a square matrix A is ivertible
More informationRademacher Complexity
EECS 598: Statistical Learig Theory, Witer 204 Topic 0 Rademacher Complexity Lecturer: Clayto Scott Scribe: Ya Deg, Kevi Moo Disclaimer: These otes have ot bee subjected to the usual scrutiy reserved for
More informationOptimum LMSE Discrete Transform
Image Trasformatio Two-dimesioal image trasforms are extremely importat areas of study i image processig. The image output i the trasformed space may be aalyzed, iterpreted, ad further processed for implemetig
More informationSequences, Mathematical Induction, and Recursion. CSE 2353 Discrete Computational Structures Spring 2018
CSE 353 Discrete Computatioal Structures Sprig 08 Sequeces, Mathematical Iductio, ad Recursio (Chapter 5, Epp) Note: some course slides adopted from publisher-provided material Overview May mathematical
More informationChapter 4. Fourier Series
Chapter 4. Fourier Series At this poit we are ready to ow cosider the caoical equatios. Cosider, for eample the heat equatio u t = u, < (4.) subject to u(, ) = si, u(, t) = u(, t) =. (4.) Here,
More informationNUMERICAL METHODS FOR SOLVING EQUATIONS
Mathematics Revisio Guides Numerical Methods for Solvig Equatios Page 1 of 11 M.K. HOME TUITION Mathematics Revisio Guides Level: GCSE Higher Tier NUMERICAL METHODS FOR SOLVING EQUATIONS Versio:. Date:
More informationEE260: Digital Design, Spring n Binary Addition. n Complement forms. n Subtraction. n Multiplication. n Inputs: A 0, B 0. n Boolean equations:
EE260: Digital Desig, Sprig 2018 EE 260: Itroductio to Digital Desig Arithmetic Biary Additio Complemet forms Subtractio Multiplicatio Overview Yao Zheg Departmet of Electrical Egieerig Uiversity of Hawaiʻi
More informationLecture Overview. 2 Permutations and Combinations. n(n 1) (n (k 1)) = n(n 1) (n k + 1) =
COMPSCI 230: Discrete Mathematics for Computer Sciece April 8, 2019 Lecturer: Debmalya Paigrahi Lecture 22 Scribe: Kevi Su 1 Overview I this lecture, we begi studyig the fudametals of coutig discrete objects.
More informationMa 530 Introduction to Power Series
Ma 530 Itroductio to Power Series Please ote that there is material o power series at Visual Calculus. Some of this material was used as part of the presetatio of the topics that follow. What is a Power
More informationSNAP Centre Workshop. Basic Algebraic Manipulation
SNAP Cetre Workshop Basic Algebraic Maipulatio 8 Simplifyig Algebraic Expressios Whe a expressio is writte i the most compact maer possible, it is cosidered to be simplified. Not Simplified: x(x + 4x)
More informationChimica Inorganica 3
himica Iorgaica Irreducible Represetatios ad haracter Tables Rather tha usig geometrical operatios, it is ofte much more coveiet to employ a ew set of group elemets which are matrices ad to make the rule
More informationRecursive Algorithm for Generating Partitions of an Integer. 1 Preliminary
Recursive Algorithm for Geeratig Partitios of a Iteger Sug-Hyuk Cha Computer Sciece Departmet, Pace Uiversity 1 Pace Plaza, New York, NY 10038 USA scha@pace.edu Abstract. This article first reviews the
More information1 Summary: Binary and Logic
1 Summary: Biary ad Logic Biary Usiged Represetatio : each 1-bit is a power of two, the right-most is for 2 0 : 0110101 2 = 2 5 + 2 4 + 2 2 + 2 0 = 32 + 16 + 4 + 1 = 53 10 Usiged Rage o bits is [0...2
More informationChapter 0. Review of set theory. 0.1 Sets
Chapter 0 Review of set theory Set theory plays a cetral role i the theory of probability. Thus, we will ope this course with a quick review of those otios of set theory which will be used repeatedly.
More informationPolynomials with Rational Roots that Differ by a Non-zero Constant. Generalities
Polyomials with Ratioal Roots that Differ by a No-zero Costat Philip Gibbs The problem of fidig two polyomials P(x) ad Q(x) of a give degree i a sigle variable x that have all ratioal roots ad differ by
More informationarxiv: v1 [math.co] 23 Mar 2016
The umber of direct-sum decompositios of a fiite vector space arxiv:603.0769v [math.co] 23 Mar 206 David Ellerma Uiversity of Califoria at Riverside August 3, 208 Abstract The theory of q-aalogs develops
More informationMachine Learning Theory Tübingen University, WS 2016/2017 Lecture 11
Machie Learig Theory Tübige Uiversity, WS 06/07 Lecture Tolstikhi Ilya Abstract We will itroduce the otio of reproducig kerels ad associated Reproducig Kerel Hilbert Spaces (RKHS). We will cosider couple
More information2.4 Sequences, Sequences of Sets
72 CHAPTER 2. IMPORTANT PROPERTIES OF R 2.4 Sequeces, Sequeces of Sets 2.4.1 Sequeces Defiitio 2.4.1 (sequece Let S R. 1. A sequece i S is a fuctio f : K S where K = { N : 0 for some 0 N}. 2. For each
More informationVolume 3, Number 2, 2017 Pages Jordan Journal of Electrical Engineering ISSN (Print): , ISSN (Online):
JJEE Volume 3, Number, 07 Pages 50-58 Jorda Joural of Electrical Egieerig ISSN (Prit: 409-9600, ISSN (Olie: 409-969 Liftig Based S-Box for Scalable Bloc Cipher Desig Based o Filter Bas Saleh S. Saraireh
More informationOPTIMAL ALGORITHMS -- SUPPLEMENTAL NOTES
OPTIMAL ALGORITHMS -- SUPPLEMENTAL NOTES Peter M. Maurer Why Hashig is θ(). As i biary search, hashig assumes that keys are stored i a array which is idexed by a iteger. However, hashig attempts to bypass
More informationUC Berkeley CS 170: Efficient Algorithms and Intractable Problems Handout 17 Lecturer: David Wagner April 3, Notes 17 for CS 170
UC Berkeley CS 170: Efficiet Algorithms ad Itractable Problems Hadout 17 Lecturer: David Wager April 3, 2003 Notes 17 for CS 170 1 The Lempel-Ziv algorithm There is a sese i which the Huffma codig was
More informationCS161: Algorithm Design and Analysis Handout #10 Stanford University Wednesday, 10 February 2016
CS161: Algorithm Desig ad Aalysis Hadout #10 Staford Uiversity Wedesday, 10 February 2016 Lecture #11: Wedesday, 10 February 2016 Topics: Example midterm problems ad solutios from a log time ago Sprig
More informationMath 155 (Lecture 3)
Math 55 (Lecture 3) September 8, I this lecture, we ll cosider the aswer to oe of the most basic coutig problems i combiatorics Questio How may ways are there to choose a -elemet subset of the set {,,,
More informationRecurrence Relations
Recurrece Relatios Aalysis of recursive algorithms, such as: it factorial (it ) { if (==0) retur ; else retur ( * factorial(-)); } Let t be the umber of multiplicatios eeded to calculate factorial(). The
More informationFortgeschrittene Datenstrukturen Vorlesung 11
Fortgeschrittee Datestruture Vorlesug 11 Schriftführer: Marti Weider 19.01.2012 1 Succict Data Structures (ctd.) 1.1 Select-Queries A slightly differet approach, compared to ra, is used for select. B represets
More information6.003 Homework #3 Solutions
6.00 Homework # Solutios Problems. Complex umbers a. Evaluate the real ad imagiary parts of j j. π/ Real part = Imagiary part = 0 e Euler s formula says that j = e jπ/, so jπ/ j π/ j j = e = e. Thus the
More informationPrinciple Of Superposition
ecture 5: PREIMINRY CONCEP O RUCUR NYI Priciple Of uperpositio Mathematically, the priciple of superpositio is stated as ( a ) G( a ) G( ) G a a or for a liear structural system, the respose at a give
More informationa for a 1 1 matrix. a b a b 2 2 matrix: We define det ad bc 3 3 matrix: We define a a a a a a a a a a a a a a a a a a
Math E-2b Lecture #8 Notes This week is all about determiats. We ll discuss how to defie them, how to calculate them, lear the allimportat property kow as multiliearity, ad show that a square matrix A
More informationThe Random Walk For Dummies
The Radom Walk For Dummies Richard A Mote Abstract We look at the priciples goverig the oe-dimesioal discrete radom walk First we review five basic cocepts of probability theory The we cosider the Beroulli
More information18th Bay Area Mathematical Olympiad. Problems and Solutions. February 23, 2016
18th Bay Area Mathematical Olympiad February 3, 016 Problems ad Solutios BAMO-8 ad BAMO-1 are each 5-questio essay-proof exams, for middle- ad high-school studets, respectively. The problems i each exam
More informationPROPERTIES OF AN EULER SQUARE
PROPERTIES OF N EULER SQURE bout 0 the mathematicia Leoard Euler discussed the properties a x array of letters or itegers ow kow as a Euler or Graeco-Lati Square Such squares have the property that every
More informationMatrices and vectors
Oe Matrices ad vectors This book takes for grated that readers have some previous kowledge of the calculus of real fuctios of oe real variable It would be helpful to also have some kowledge of liear algebra
More information(3) If you replace row i of A by its sum with a multiple of another row, then the determinant is unchanged! Expand across the i th row:
Math 50-004 Tue Feb 4 Cotiue with sectio 36 Determiats The effective way to compute determiats for larger-sized matrices without lots of zeroes is to ot use the defiitio, but rather to use the followig
More informationComplex Stochastic Boolean Systems: Generating and Counting the Binary n-tuples Intrinsically Less or Greater than u
Proceedigs of the World Cogress o Egieerig ad Computer Sciece 29 Vol I WCECS 29, October 2-22, 29, Sa Fracisco, USA Complex Stochastic Boolea Systems: Geeratig ad Coutig the Biary -Tuples Itrisically Less
More informationCHAPTER 10 INFINITE SEQUENCES AND SERIES
CHAPTER 10 INFINITE SEQUENCES AND SERIES 10.1 Sequeces 10.2 Ifiite Series 10.3 The Itegral Tests 10.4 Compariso Tests 10.5 The Ratio ad Root Tests 10.6 Alteratig Series: Absolute ad Coditioal Covergece
More informationSome Explicit Formulae of NAF and its Left-to-Right. Analogue Based on Booth Encoding
Vol.7, No.6 (01, pp.69-74 http://dx.doi.org/10.1457/ijsia.01.7.6.7 Some Explicit Formulae of NAF ad its Left-to-Right Aalogue Based o Booth Ecodig Dog-Guk Ha, Okyeo Yi, ad Tsuyoshi Takagi Kookmi Uiversity,
More informationIntroduction to Computational Biology Homework 2 Solution
Itroductio to Computatioal Biology Homework 2 Solutio Problem 1: Cocave gap pealty fuctio Let γ be a gap pealty fuctio defied over o-egative itegers. The fuctio γ is called sub-additive iff it satisfies
More informationDeterminants of order 2 and 3 were defined in Chapter 2 by the formulae (5.1)
5. Determiats 5.. Itroductio 5.2. Motivatio for the Choice of Axioms for a Determiat Fuctios 5.3. A Set of Axioms for a Determiat Fuctio 5.4. The Determiat of a Diagoal Matrix 5.5. The Determiat of a Upper
More informationOblivious Transfer using Elliptic Curves
Oblivious Trasfer usig Elliptic Curves bhishek Parakh Louisiaa State Uiversity, ato Rouge, L May 4, 006 bstract: This paper proposes a algorithm for oblivious trasfer usig elliptic curves lso, we preset
More informationLecture 23 Rearrangement Inequality
Lecture 23 Rearragemet Iequality Holde Lee 6/4/ The Iequalities We start with a example Suppose there are four boxes cotaiig $0, $20, $50 ad $00 bills, respectively You may take 2 bills from oe box, 3
More information(3) If you replace row i of A by its sum with a multiple of another row, then the determinant is unchanged! Expand across the i th row:
Math 5-4 Tue Feb 4 Cotiue with sectio 36 Determiats The effective way to compute determiats for larger-sized matrices without lots of zeroes is to ot use the defiitio, but rather to use the followig facts,
More informationCSE 191, Class Note 05: Counting Methods Computer Sci & Eng Dept SUNY Buffalo
Coutig Methods CSE 191, Class Note 05: Coutig Methods Computer Sci & Eg Dept SUNY Buffalo c Xi He (Uiversity at Buffalo CSE 191 Discrete Structures 1 / 48 Need for Coutig The problem of coutig the umber
More informationORTHOGONAL MATRIX IN CRYPTOGRAPHY
Orthogoal Matrix i Cryptography ORTHOGONAL MATRIX IN CRYPTOGRAPHY Yeray Cachó Sataa Member of CriptoRed (U.P.M.) ABSTRACT I this work is proposed a method usig orthogoal matrix trasform properties to ecrypt
More informationAxioms of Measure Theory
MATH 532 Axioms of Measure Theory Dr. Neal, WKU I. The Space Throughout the course, we shall let X deote a geeric o-empty set. I geeral, we shall ot assume that ay algebraic structure exists o X so that
More informationMatrix Algebra 2.2 THE INVERSE OF A MATRIX Pearson Education, Inc.
2 Matrix Algebra 2.2 THE INVERSE OF A MATRIX MATRIX OPERATIONS A matrix A is said to be ivertible if there is a matrix C such that CA = I ad AC = I where, the idetity matrix. I = I I this case, C is a
More informationDIFFERENTIAL CRYPTANALYSIS FOR A 3-ROUND SPN
IRNTIL RYPTNLYSIS OR -ROUN SPN M. Tolga Sakallı rca uluş daç Şahi atma üyüksaraçoğlu e-mail: tolga@trakya.edu.tr. e-mail: ercab@trakya.edu.tr e-mail: adacs@trakya.edu.tr e-mail: fbuyuksaracoglu@trakya.edu.tr
More informationLinear Regression Demystified
Liear Regressio Demystified Liear regressio is a importat subject i statistics. I elemetary statistics courses, formulae related to liear regressio are ofte stated without derivatio. This ote iteds to
More informationLecture Notes for Analysis Class
Lecture Notes for Aalysis Class Topological Spaces A topology for a set X is a collectio T of subsets of X such that: (a) X ad the empty set are i T (b) Uios of elemets of T are i T (c) Fiite itersectios
More informationSECTION 1.5 : SUMMATION NOTATION + WORK WITH SEQUENCES
SECTION 1.5 : SUMMATION NOTATION + WORK WITH SEQUENCES Read Sectio 1.5 (pages 5 9) Overview I Sectio 1.5 we lear to work with summatio otatio ad formulas. We will also itroduce a brief overview of sequeces,
More informationInjections, Surjections, and the Pigeonhole Principle
Ijectios, Surjectios, ad the Pigeohole Priciple 1 (10 poits Here we will come up with a sloppy boud o the umber of parethesisestigs (a (5 poits Describe a ijectio from the set of possible ways to est pairs
More informationMA131 - Analysis 1. Workbook 10 Series IV
MA131 - Aalysis 1 Workbook 10 Series IV Autum 2004 Cotets 4.19 Rearragemets of Series...................... 1 4.19 Rearragemets of Series If you take ay fiite set of umbers ad rearrage their order, their
More informationCALCULATION OF FIBONACCI VECTORS
CALCULATION OF FIBONACCI VECTORS Stuart D. Aderso Departmet of Physics, Ithaca College 953 Daby Road, Ithaca NY 14850, USA email: saderso@ithaca.edu ad Dai Novak Departmet of Mathematics, Ithaca College
More information3.1 Counting Principles
3.1 Coutig Priciples Goal: Cout the umber of objects i a set. Notatio: Whe S is a set, S deotes the umber of objects i the set. This is also called S s cardiality. Additio Priciple: Whe you wat to cout
More informationProduct measures, Tonelli s and Fubini s theorems For use in MAT3400/4400, autumn 2014 Nadia S. Larsen. Version of 13 October 2014.
Product measures, Toelli s ad Fubii s theorems For use i MAT3400/4400, autum 2014 Nadia S. Larse Versio of 13 October 2014. 1. Costructio of the product measure The purpose of these otes is to preset the
More information, then cv V. Differential Equations Elements of Lineaer Algebra Name: Consider the differential equation. and y2 cos( kx)
Cosider the differetial equatio y '' k y 0 has particular solutios y1 si( kx) ad y cos( kx) I geeral, ay liear combiatio of y1 ad y, cy 1 1 cy where c1, c is also a solutio to the equatio above The reaso
More informationEDGE AND SECANT IDEALS OF SHARED-VERTEX GRAPHS
EDGE AND SECANT IDEALS OF SHARED-VERTEX GRAPHS ZVI ROSEN Abstract. We examie miimal free resolutios ad Betti diagrams of the edge ad secat ideals of oe family of graphs. We demostrate how splittig the
More informationChapter 2 The Solution of Numerical Algebraic and Transcendental Equations
Chapter The Solutio of Numerical Algebraic ad Trascedetal Equatios Itroductio I this chapter we shall discuss some umerical methods for solvig algebraic ad trascedetal equatios. The equatio f( is said
More information6 Integers Modulo n. integer k can be written as k = qn + r, with q,r, 0 r b. So any integer.
6 Itegers Modulo I Example 2.3(e), we have defied the cogruece of two itegers a,b with respect to a modulus. Let us recall that a b (mod ) meas a b. We have proved that cogruece is a equivalece relatio
More informationLet us consider the following problem to warm up towards a more general statement.
Lecture 4: Sequeces with repetitios, distributig idetical objects amog distict parties, the biomial theorem, ad some properties of biomial coefficiets Refereces: Relevat parts of chapter 15 of the Math
More informationARRANGEMENTS IN A CIRCLE
ARRANGEMENTS IN A CIRCLE Whe objects are arraged i a circle, the total umber of arragemets is reduced. The arragemet of (say) four people i a lie is easy ad o problem (if they liste of course!!). With
More information3. Z Transform. Recall that the Fourier transform (FT) of a DT signal xn [ ] is ( ) [ ] = In order for the FT to exist in the finite magnitude sense,
3. Z Trasform Referece: Etire Chapter 3 of text. Recall that the Fourier trasform (FT) of a DT sigal x [ ] is ω ( ) [ ] X e = j jω k = xe I order for the FT to exist i the fiite magitude sese, S = x [
More informationNICK DUFRESNE. 1 1 p(x). To determine some formulas for the generating function of the Schröder numbers, r(x) = a(x) =
AN INTRODUCTION TO SCHRÖDER AND UNKNOWN NUMBERS NICK DUFRESNE Abstract. I this article we will itroduce two types of lattice paths, Schröder paths ad Ukow paths. We will examie differet properties of each,
More informationAnalysis of Experimental Measurements
Aalysis of Experimetal Measuremets Thik carefully about the process of makig a measuremet. A measuremet is a compariso betwee some ukow physical quatity ad a stadard of that physical quatity. As a example,
More informationThe Discrete Fourier Transform
The Discrete Fourier Trasform Complex Fourier Series Represetatio Recall that a Fourier series has the form a 0 + a k cos(kt) + k=1 b k si(kt) This represetatio seems a bit awkward, sice it ivolves two
More informationProbability, Expectation Value and Uncertainty
Chapter 1 Probability, Expectatio Value ad Ucertaity We have see that the physically observable properties of a quatum system are represeted by Hermitea operators (also referred to as observables ) such
More informationECEN 655: Advanced Channel Coding Spring Lecture 7 02/04/14. Belief propagation is exact on tree-structured factor graphs.
ECEN 655: Advaced Chael Codig Sprig 014 Prof. Hery Pfister Lecture 7 0/04/14 Scribe: Megke Lia 1 4-Cycles i Gallager s Esemble What we already kow: Belief propagatio is exact o tree-structured factor graphs.
More informationPolynomial identity testing and global minimum cut
CHAPTER 6 Polyomial idetity testig ad global miimum cut I this lecture we will cosider two further problems that ca be solved usig probabilistic algorithms. I the first half, we will cosider the problem
More information10. Comparative Tests among Spatial Regression Models. Here we revisit the example in Section 8.1 of estimating the mean of a normal random
Part III. Areal Data Aalysis 0. Comparative Tests amog Spatial Regressio Models While the otio of relative likelihood values for differet models is somewhat difficult to iterpret directly (as metioed above),
More informationµ and π p i.e. Point Estimation x And, more generally, the population proportion is approximately equal to a sample proportion
Poit Estimatio Poit estimatio is the rather simplistic (ad obvious) process of usig the kow value of a sample statistic as a approximatio to the ukow value of a populatio parameter. So we could for example
More informationInfinite Sequences and Series
Chapter 6 Ifiite Sequeces ad Series 6.1 Ifiite Sequeces 6.1.1 Elemetary Cocepts Simply speakig, a sequece is a ordered list of umbers writte: {a 1, a 2, a 3,...a, a +1,...} where the elemets a i represet
More informationProof of Goldbach s Conjecture. Reza Javaherdashti
Proof of Goldbach s Cojecture Reza Javaherdashti farzijavaherdashti@gmail.com Abstract After certai subsets of Natural umbers called Rage ad Row are defied, we assume (1) there is a fuctio that ca produce
More informationRandom Matrices with Blocks of Intermediate Scale Strongly Correlated Band Matrices
Radom Matrices with Blocks of Itermediate Scale Strogly Correlated Bad Matrices Jiayi Tog Advisor: Dr. Todd Kemp May 30, 07 Departmet of Mathematics Uiversity of Califoria, Sa Diego Cotets Itroductio Notatio
More informationKinetics of Complex Reactions
Kietics of Complex Reactios by Flick Colema Departmet of Chemistry Wellesley College Wellesley MA 28 wcolema@wellesley.edu Copyright Flick Colema 996. All rights reserved. You are welcome to use this documet
More informationHow to Maximize a Function without Really Trying
How to Maximize a Fuctio without Really Tryig MARK FLANAGAN School of Electrical, Electroic ad Commuicatios Egieerig Uiversity College Dubli We will prove a famous elemetary iequality called The Rearragemet
More informationDefinition 4.2. (a) A sequence {x n } in a Banach space X is a basis for X if. unique scalars a n (x) such that x = n. a n (x) x n. (4.
4. BASES I BAACH SPACES 39 4. BASES I BAACH SPACES Sice a Baach space X is a vector space, it must possess a Hamel, or vector space, basis, i.e., a subset {x γ } γ Γ whose fiite liear spa is all of X ad
More informationFirst, note that the LS residuals are orthogonal to the regressors. X Xb X y = 0 ( normal equations ; (k 1) ) So,
0 2. OLS Part II The OLS residuals are orthogoal to the regressors. If the model icludes a itercept, the orthogoality of the residuals ad regressors gives rise to three results, which have limited practical
More informationTopic 5: Basics of Probability
Topic 5: Jue 1, 2011 1 Itroductio Mathematical structures lie Euclidea geometry or algebraic fields are defied by a set of axioms. Mathematical reality is the developed through the itroductio of cocepts
More informationMath 508 Exam 2 Jerry L. Kazdan December 9, :00 10:20
Math 58 Eam 2 Jerry L. Kazda December 9, 24 9: :2 Directios This eam has three parts. Part A has 8 True/False questio (2 poits each so total 6 poits), Part B has 5 shorter problems (6 poits each, so 3
More informationOptimization Methods: Linear Programming Applications Assignment Problem 1. Module 4 Lecture Notes 3. Assignment Problem
Optimizatio Methods: Liear Programmig Applicatios Assigmet Problem Itroductio Module 4 Lecture Notes 3 Assigmet Problem I the previous lecture, we discussed about oe of the bech mark problems called trasportatio
More information1. By using truth tables prove that, for all statements P and Q, the statement
Author: Satiago Salazar Problems I: Mathematical Statemets ad Proofs. By usig truth tables prove that, for all statemets P ad Q, the statemet P Q ad its cotrapositive ot Q (ot P) are equivalet. I example.2.3
More informationSOME TRIBONACCI IDENTITIES
Mathematics Today Vol.7(Dec-011) 1-9 ISSN 0976-38 Abstract: SOME TRIBONACCI IDENTITIES Shah Devbhadra V. Sir P.T.Sarvajaik College of Sciece, Athwalies, Surat 395001. e-mail : drdvshah@yahoo.com The sequece
More informationNumber Representation
Number Represetatio 1 Number System :: The Basics We are accustomed to usig the so-called decimal umber system Te digits :: 0,1,2,3,4,5,6,7,8,9 Every digit positio has a weight which is a power of 10 Base
More information4 The Sperner property.
4 The Sperer property. I this sectio we cosider a surprisig applicatio of certai adjacecy matrices to some problems i extremal set theory. A importat role will also be played by fiite groups. I geeral,
More informationCounting Well-Formed Parenthesizations Easily
Coutig Well-Formed Parethesizatios Easily Pekka Kilpeläie Uiversity of Easter Filad School of Computig, Kuopio August 20, 2014 Abstract It is well kow that there is a oe-to-oe correspodece betwee ordered
More informationAppendix: The Laplace Transform
Appedix: The Laplace Trasform The Laplace trasform is a powerful method that ca be used to solve differetial equatio, ad other mathematical problems. Its stregth lies i the fact that it allows the trasformatio
More information1 Last time: similar and diagonalizable matrices
Last time: similar ad diagoalizable matrices Let be a positive iteger Suppose A is a matrix, v R, ad λ R Recall that v a eigevector for A with eigevalue λ if v ad Av λv, or equivaletly if v is a ozero
More information