Number Theoretic Methods in Cryptography: Complexity lower bounds (Progress in Computer Science and Applied Logic) Buy on Amazon
Facebook LinkedIn

Number Theoretic Methods in Cryptography: Complexity lower bounds (Progress in Computer Science and Applied Logic)

Publisher Birkhäuser
Category Computers
179.00 USD

Usually ships in 24 hours

Book Details
Author(s) Igor Shparlinski
Publisher Birkhäuser
ISBN / ASIN 3764358882
ISBN-13 9783764358884
Availability Usually ships in 24 hours
Sales Rank #13,439,734
Category Computers
Marketplace United States 🇺🇸
Description
The book introduces new techniques which imply rigorous lower bounds on the complexity of some number theoretic and cryptographic problems. These methods and techniques are based on bounds of character sums and numbers of solutions of some polynomial equations over finite fields and residue rings. It also contains a number of open problems and proposals for further research. We obtain several lower bounds, exponential in terms of logp, on the de­ grees and orders of • polynomials; • algebraic functions; • Boolean functions; • linear recurring sequences; coinciding with values of the discrete logarithm modulo a prime p at suf­ ficiently many points (the number of points can be as small as pI/He). These functions are considered over the residue ring modulo p and over the residue ring modulo an arbitrary divisor d of p - 1. The case of d = 2 is of special interest since it corresponds to the representation of the right­ most bit of the discrete logarithm and defines whether the argument is a quadratic residue. We also obtain non-trivial upper bounds on the de­ gree, sensitivity and Fourier coefficients of Boolean functions on bits of x deciding whether x is a quadratic residue. These results are used to obtain lower bounds on the parallel arithmetic and Boolean complexity of computing the discrete logarithm. For example, we prove that any unbounded fan-in Boolean circuit. of sublogarithmic depth computing the discrete logarithm modulo p must be of superpolynomial size.
Donate to EbookNetworking
Previous Book Sams Teach Yourself XML in ... Next Book Machine Learning and Knowle...
Previous Sams Teach Yourse...
Next Machine Learning ...