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Computational indistinguishability: a sample hierarchy

机译:计算脱节性:样本层次结构

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We consider the existence of pairs of probability ensembles which may be efficiently distinguished from each other given k samples but cannot be efficiently distinguished given k'k samples. If is well known that in any such pair of ensembles it cannot be that both are efficiently computable (and that such phenomena cannot exist for non-uniform classes of distinguishers, say, polynomial-size circuits). It was also known that there exist pairs of ensembles which may be efficiently distinguished based on two samples but cannot be efficiently distinguished based on a single sample. In contrast, it was not known whether the distinguishing power increases when one moves from two samples to polynomially-many samples. We show the existence of pairs of ensembles which may be efficiently distinguished given k+1 samples but cannot be efficiently distinguished given k samples, where k can be any function bounded above by a polynomial in the security parameter. In course of establishing the above result, we prove several technical lemmas regarding polynomials and graphs. We believe that these may be of independent interest.
机译:我们考虑存在对概率集成的概率集成,这可以有效地与给定的K样品彼此有效地区分,但不能有效地区分给定K'> K样品。如果众所周知,在任何这样的组合中,它不能是两者都是有效的可计算的(并且这种现象不能存在于非均匀类别的区分器中,例如多项式尺寸电路)。还已知存在基于两个样本可以有效地区分的一对合奏,而是基于单个样本可以有效地区分。相反,当从两个样本移动到多项式样本时,尚不知道区分功率是否增加。我们展示了可以有效地区分给定的K + 1样本的合奏成对的存在,而是不能有效地区分给定的k个样本,其中k可以是安全参数中的多项式界定的任何功能。在建立上述结果,我们证明了关于多项式和图形的技术lemmas。我们认为这些可能是独立的利益。

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