Discrete Mathematics With Cryptographic Applications
A Self-Teaching Introduction
- Publisher
Mercury Learning and Information - Published
22nd October 2021 - ISBN 9781683927631
- Language English
- Pages 375 pp.
- Size 7" x 9"
- Request Exam Copy
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- Publisher
Mercury Learning and Information - Published
20th September 2021 - ISBN 9781683927617
- Language English
- Pages 375 pp.
- Size 7" x 9"
- Request E-Exam Copy
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- Publisher
Mercury Learning and Information - Published
20th September 2021 - ISBN 9781683927624
- Language English
- Pages 375 pp.
- Size 7" x 9"
This book covers discrete mathematics both as it has been established after its emergence since the middle of the last century and as its elementary applications to cryptography. It can be used by any individual studying discrete mathematics, finite mathematics, and similar subjects. Any necessary prerequisites are explained and illustrated in the book. As a background of cryptography, the textbook gives an introduction into number theory, coding theory, information theory, that obviously have discrete nature.
FEATURES:
- Designed in a “self-teaching” format, the book includes about 600 problems (with and without solutions) and numerous examples of cryptography
- Covers cryptography topics such as CRT, affine ciphers, hashing functions, substitution ciphers, unbreakable ciphers, Discrete Logarithm Problem (DLP), and more.
1: Elementary Functions
2: Propositional Algebra
3: Naïve and Formal (Axiomatic) Set Theory
4: Mappings; Groups, Rings, and Fields; Matrices and Determinants
5: Predicates and Quantifiers
6: Binary Relations and Relational Databases
7: Combinatorics
8. Number Theory
9: Boolean Functions
10: Hashing Functions and Cryptographic Maps
11: Generating Polynomials and Inversion Formulas
12: Systems of Representatives
13: Boolean Algebras
14: Combinatorial Circuits
15: Complete Systems of Boolean Functions
16: Graph Theory
17: Trees and Digraphs
18: Computations and Algorithms
19: Finite Automata
20: Game Theory
21: Information Theory and Coding
22: Probability Theory with a Finite Sample Space
23: Turing Machines, P, NP Classes and Other Models
24: Answers and Solutions to Selected Exercises
Index
Alexander I. Kheyfits, PhD
Alexander I. Kheyfits, Ph.D. taught undergraduate and graduate-level subjects in mathematics and computer science for over thirty years at various CUNY colleges, including the CUNY Graduate Center and Bronx Community College. He has published several books, over forty journal articles, and has been an invited speaker at numerous conferences.