Computational indistinguishability

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In computational complexity, if and are two distribution ensembles indexed by a security parameter n (which usually refers to the length of the input), then 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:

denoted .[1] In other words, every efficient algorithm A's behavior does not significantly change when given samples according to Dn or En in the limit as . 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, , 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.[2]:107 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.[2]:108


  1. ^ Lecture 4 - Computational Indistinguishability, Pseudorandom Generators
  2. ^ a b Goldreich, O. (2003). Foundations of cryptography. Cambridge, UK: Cambridge University Press.

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This article incorporates material from computationally indistinguishable on PlanetMath, which is licensed under the Creative Commons Attribution/Share-Alike License.