Digital Signatures - download pdf or read online

By Jonathan Katz (auth.)

ISBN-10: 0387277110

ISBN-13: 9780387277110

Digital Signatures is the 1st accomplished account of the theoretical ideas and methods utilized in the layout of provably safe signature schemes. as well as supplying the reader with a greater figuring out of the safety promises supplied by means of electronic signatures, the e-book additionally includes complete descriptions and targeted proofs for primarily all recognized safe signature schemes within the cryptographic literature. A invaluable reference for college kids, professors, and researchers, electronic Signature Schemes can be utilized for self-study, as a complement to a path on theoretical cryptography, or as a textbook in a graduate-level seminar.

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F , the evaluation algorithm, is a deterministic algorithm that takes as input parameters I and an element x ∈ DI , and outputs an element y ∈ RI . We write this as y := fI (x). ) Π is a permutation family if the following additionally hold: 1. For all I output by Gen, the distribution defined by the output of Samp(I) is (statistically close to) the uniform distribution on DI . 2. For all I output by Gen it holds that DI = RI and the function fI is a bijection. If Π is a permutation family and there exists a polynomial p such that DI = {0, 1} p(k) for all I output by Gen(1k ), then we say that Π is a permutation family over bit-strings.

Samp, the sampling algorithm, is a probabilistic algorithm that takes as input parameters I and outputs an element x ∈ DI whose distribution is statistically close to the uniform distribution over DI . We usually leave Samp implicit, and just write x ← DI . • f0 and f1 , the evaluation algorithms, are deterministic algorithms that take as input parameters I and an element x ∈ DI , and output an element y ∈ DI . We write this as y := fI,0 (x) or y := fI,1 (x). 42 2 Cryptographic Hardness Assumptions • The following is negligible for all PPT algorithms A: Pr (I, td) ← Gen(1k ); (x0 , x1 ) ← A(I) : fI,0 (x0 ) = fI,1 (x1 ) .

The distribution on r is statistically close to uniform.

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Digital Signatures by Jonathan Katz (auth.)

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