A primer on pseudorandom generators by Oded Goldreich

By Oded Goldreich

A clean examine the query of randomness used to be taken within the conception of computing: A distribution is pseudorandom if it can't be exotic from the uniform distribution through any effective process. This paradigm, initially associating effective systems with polynomial-time algorithms, has been utilized with recognize to a number of average periods of distinguishing tactics. The ensuing idea of pseudorandomness is appropriate to technological know-how at huge and is heavily on the topic of valuable parts of computing device technological know-how, akin to algorithmic layout, complexity concept, and cryptography. This primer surveys the speculation of pseudorandomness, beginning with the final paradigm, and discussing a variety of incarnations whereas emphasizing the case of general-purpose pseudorandom turbines (withstanding any polynomial-time distinguisher). extra issues contain the "derandomization" of arbitrary probabilistic polynomial-time algorithms, pseudorandom turbines withstanding space-bounded distinguishers, and several other common notions of special-purpose pseudorandom turbines. The primer assumes simple familiarity with the thought of effective algorithms and with effortless likelihood idea, yet offers a simple creation to all notions which are truly used. for that reason, the primer is largely self-contained, even though the reader is now and then observed different assets for extra element

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Using the following three steps, prove that the existence of polynomial-time constructible probability ensembles that are statistically far apart and yet are computationally indistinguishable implies the existence of pseudorandom generators. 1. Show that, without loss of generality, we may assume that the variation distance between Xn and Yn is greater than 1 − exp(−n). , Yn ), and t(n) = O(n · p(n)2 ). , Y n ) that reside in Sn . EXERCISES 33 2. Using {Xn }n∈N and {Yn }n∈N as in Step 1, prove the existence of a false entropy generator, where a false entropy generator is a deterministic polynomial-time algorithm G such that G(Uk ) has entropy e(k) but {G(Uk )}k∈N is computationally indistinguishable from a polynomial-time constructible ensemble that has entropy greater than e(·) + (1/2).

On the other hand, the time-complexity of G implies that the straightforward deterministic emulation of AG (x) takes time 2k · (poly(2k · ℓ(k)) + t(n)), which is upper-bounded by poly(2k · −1 ℓ(k)) = poly(2ℓ (t(n)) ·t(n)). This yields the following (conditional) derandomization result. 2 (using canonical derandomizers): Let ℓ, t : N → N be monotonically increasing functions and let ℓ−1 (t(n)) denote the smallest integer k such that ℓ(k) ≥ t(n). If there exists a canonical derandomizer of stretch ℓ, then, for every time-constructible t : N → N, it holds that BPtime(t) ⊆ Dtime(T ), where T (n) = −1 poly(2ℓ (t(n)) · t(n)).

4] (or [22, Sec. 3]). Conclusion. 1, we may ignore the specific stretch function. 5. 5 21 Constructions So far we have ignored the basic question of whether pseudorandom generators exist at all. 14). Thus, the existence of functions that are easy to compute but hard to invert, called one-way functions, is a necessary condition to the existence of pseudorandom generators, Interestingly, this condition is also sufficient; that is, pseudorandom generators can be constructed based on any one-way function.

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