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Computational indistinguishability : ウィキペディア英語版
Computational indistinguishability
In computational complexity, if \scriptstyle\__(A(x) = 1 ) - \Pr_(A(x) = 1 ) \right|.
denoted D_n \approx E_n\!\,.〔(Lecture 4 - Computational Indistinguishability, Pseudorandom Generators )〕 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 such algorithm will only perform negligibly better than if one were to just guess.
Implicit in the definition is the condition that the algorithm, A, must decide based on a single sample from one of the distributions. One might conceive of a situation in which the algorithm trying to distinguish between two distributions, could access as many samples as it needed. Hence two ensembles that cannot be distinguished by polynomial-time algorithms looking at multiple samples are deemed indistinguishable by polynomial-time sampling.〔Goldreich, O. (2003). Foundations of cryptography. Cambridge, UK: Cambridge University Press.〕 If the polynomial-time algorithm can generate samples in polynomial time, or has access to a random oracle that generates samples for it, then indistinguishability by polynomial-time sampling is equivalent to computational indistinguishability.〔
== References ==

* Donald Beaver and Silvio Micali and Phillip Rogaway, The Round Complexity of Secure Protocols (Extended Abstract), 1990, pp. 503–513
* Shafi Goldwasser and Silvio Micali. Probabilistic Encryption. JCSS, 28(2):270–299, 1984
* Oded Goldreich. Foundations of Cryptography: Volume 2 – Basic Applications. Cambridge University Press, 2004.
* Jonathan Katz, Yehuda Lindell, "Introduction to Modern Cryptography: Principles and Protocols," Chapman & Hall/CRC, 2007

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