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Pseudorandom Ensemble
In cryptography, a pseudorandom ensemble is a family of variables meeting the following criteria: Let U = \_ be a uniform ensemble and X = \_ be an ensemble Ensemble may refer to: Art * Architectural ensemble * ''Ensemble'' (album), Kendji Girac 2015 album * Ensemble (band), a project of Olivier Alary * Ensemble cast (drama, comedy) * Ensemble (musical theatre), also known as the chorus * ''En .... The ensemble X is called pseudorandom if X and U are indistinguishable in polynomial time. References * Goldreich, Oded (2001). ''Foundations of Cryptography: Volume 1, Basic Tools''. Cambridge University Press. . Fragments available at theauthor's web site Algorithmic information theory Pseudorandomness Cryptography {{crypto-stub ...
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Uniform Ensemble
In cryptography, a distribution ensemble or probability ensemble is a family of distributions or random variables X = \_ where I is a (countable) index set, and each X_i is a random variable, or probability distribution. Often I=\N and it is required that each X_n have a certain property for ''n'' sufficiently large. For example, a uniform ensemble U = \_ is a distribution ensemble where each U_n is uniformly distributed over strings of length ''n''. In fact, many applications of probability ensembles implicitly assume that the probability spaces for the random variables all coincide in this way, so every probability ensemble is also a stochastic process. See also * Provable security * Statistically close * Pseudorandom ensemble * Computational indistinguishability In computational complexity and cryptography, two families of distributions are computationally indistinguishable if no efficient algorithm can tell the difference between them except with negligible probability. ...
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Distribution Ensemble
In cryptography, a distribution ensemble or probability ensemble is a family of distributions or random variables X = \_ where I is a (countable) index set, and each X_i is a random variable, or probability distribution. Often I=\N and it is required that each X_n have a certain property for ''n'' sufficiently large. For example, a uniform ensemble U = \_ is a distribution ensemble where each U_n is uniformly distributed over strings of length ''n''. In fact, many applications of probability ensembles implicitly assume that the probability spaces for the random variables all coincide in this way, so every probability ensemble is also a stochastic process. See also * Provable security * Statistically close * Pseudorandom ensemble * Computational indistinguishability In computational complexity and cryptography, two families of distributions are computationally indistinguishable if no efficient algorithm can tell the difference between them except with negligible probabili ...
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Computationally Indistinguishable
In computational complexity and cryptography, two families of distributions are computationally indistinguishable if no efficient algorithm can tell the difference between them except with negligible probability. Formal definition Let \scriptstyle\_ and \scriptstyle\_ be two distribution ensembles indexed by a security parameter ''n'' (which usually refers to the length of the input); we say they are computationally indistinguishable if for any non-uniform probabilistic polynomial time algorithm ''A'', the following quantity is a negligible function in ''n'': : \delta(n) = \left, \Pr_ A(x) = 1- \Pr_ A(x) = 1\. denoted D_n \approx E_n. In other words, every efficient algorithm ''As behavior does not significantly change when given samples according to ''D''''n'' or ''E''''n'' in the limit as n\to \infty. Another interpretation of computational indistinguishability, is that polynomial-time algorithms actively trying to distinguish between the two ensembles cannot do so: that any s ...
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Algorithmic Information Theory
Algorithmic information theory (AIT) is a branch of theoretical computer science that concerns itself with the relationship between computation and information of computably generated objects (as opposed to stochastically generated), such as strings or any other data structure. In other words, it is shown within algorithmic information theory that computational incompressibility "mimics" (except for a constant that only depends on the chosen universal programming language) the relations or inequalities found in information theory. According to Gregory Chaitin, it is "the result of putting Shannon's information theory and Turing's computability theory into a cocktail shaker and shaking vigorously." Besides the formalization of a universal measure for irreducible information content of computably generated objects, some main achievements of AIT were to show that: in fact algorithmic complexity follows (in the self-delimited case) the same inequalities (except for a constant) tha ...
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Pseudorandomness
A pseudorandom sequence of numbers is one that appears to be statistically random, despite having been produced by a completely deterministic and repeatable process. Background The generation of random numbers has many uses, such as for random sampling, Monte Carlo methods, board games, or gambling. In physics, however, most processes, such as gravitational acceleration, are deterministic, meaning that they always produce the same outcome from the same starting point. Some notable exceptions are radioactive decay and quantum measurement, which are both modeled as being truly random processes in the underlying physics. Since these processes are not practical sources of random numbers, people use pseudorandom numbers, which ideally have the unpredictability of a truly random sequence, despite being generated by a deterministic process. In many applications, the deterministic process is a computer algorithm called a pseudorandom number generator, which must first be provided wi ...
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