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Pseudorandom Generators with Optimal Seed Length for Non-Boolean Poly-Size Circuits

机译:用于非布尔多尺寸电路的具有最佳种子长度的伪随机发生器

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A sampling procedure for a distribution P over {0,1}' is a function C : {0, 1}~n → {0,1}~t such that the distribution C(U_n) (obtained by applying C on the uniform distribution U~n) is the "desired distribution" P. Let n > r ≥ t = n~(Ω(1)). An ∈-nb-PRG (denned by Dubrov and Ishai [2006]) is a function G : {0, 1}~r→ {0,1}~n such that for every C : {0,1}~n → {0, 1}~t in some class of "interesting sampling procedures," C'(U_r) = C(GiU_r)) is ∈-close to C(U~n) in statistical distance. We construct poly-time computable nb-PRGs with r = O(t) for poly-size circuits relying on the assumption that there exists β > 0 and a problem L in E = DTIME(2~(O(n))) such that for every large enough n, nonde-terministic circuits of size 2~(βn) that have NP-gates cannot solve L on inputs of length n. This assumption is a scaled nonuniform analog of (the widely believed) EXP ≠ Σ(~p_2), and similar assumptions appear in various contexts in derandomization. Previous nb-PRGs of Dubrov and Ishai have r = Ω(t~2) and are based on very strong cryptographic assumptions or, alternatively, on nonstandard assumptions regarding incompressibility of functions on random inputs. When restricting to poly-size circuits C : [0, 1]~n → {0, 1}~t with Shannon entropy H(C(U_n)) ≤k,for t.>k = n~(Ω(1)), our nb-PRGs have r = O(k). The nb-PRGs of Dubrov and Ishai use seed length r = Q(k~2) and require that the probability distribution of C(U_n) is efficiently computable. Our nb-PRGs follow from a notion of "conditional PRGs," which may be of independent interest. These are PRGs where G(U_r) remains pseudorandom even when conditioned on a "large" event {A(G(U_r)) = 1), for an arbitrary poly-size circuit A A related notion was considered by Shaltiel and Umans [2005] in a different setting, and our proofs use ideas from that paper, as well as ideas of Dubrov and Ishai. We also give an unconditional construction of poly-time computable nb-PRGs for poly(n)-size, depth d circuits C : {0,1}~n → {0,1}~t with r = O(l log~(d+O(1) n). This improves upon the previous work of Dubrov and Ishai that has r ≥ t~2. This result follows by adapting a recent PRG construction of Trevisan and Xue [2013] to the case of nb-PRGs. We also show that this PRG can be implemented by a uniform family of constant-depth circuits with slightly increased seed length.
机译:{0,1}'上的分布P的采样过程是函数C:{0,1}〜n→{0,1}〜t,使得分布C(U_n)(通过将C应用于均匀分布U〜n)是“期望分布”P。令n> r≥t = n〜(Ω(1))。 ∈-nb-PRG(由Dubrov和Ishai定义[2006])是一个函数G:{0,1}〜r→{0,1}〜n使得对于每个C:{0,1}〜n→ {0,1}〜t在某类“有趣的采样过程”中,C'(U_r)= C(GiU_r))在统计距离上接近于C(U〜n)。我们基于存在β> 0且E = DTIME(2〜(O(n)))中存在问题L的假设,为多尺寸电路构造了r = O(t)的可乘时间可计算nb-PRG。对于每一个足够大的n,具有NP门的大小为2〜(βn)的不确定电路无法在长度为n的输入上求解L.该假设是(普遍认为的)EXP≠Σ(〜p_2)的缩放非均匀模拟,并且类似的假设出现在非随机化的各种情况下。杜布罗夫(Dubrov)和伊斯海(Ishai)的先前nb-PRG具有r =Ω(t〜2),并且基于非常强的密码学假设,或者基于关于随机输入函数不可压缩性的非标准假设。当限制为多尺寸电路C时:[0,1]〜n→{0,1}〜t,且香农熵H(C(U_n))≤k,对于t> k = n〜(Ω(1) ),我们的nb-PRG的r = O(k)。 Dubrov和Ishai的nb-PRG使用种子长度r = Q(k〜2),并要求C(U_n)的概率分布是可有效计算的。我们的nb-PRG遵循“条件性PRG”的概念,这可能是独立利益。这些是PRG,即使条件是“大”事件(A(G(U_r))= 1),G(U_r)仍是伪随机的,Shaltiel和Umans [2005]考虑了任意多尺寸电路AA相关概念。在不同的背景下,我们的证明使用了该论文中的思想,以及杜布罗夫和伊斯海的思想。我们还给出了无条件构造的多时间可计算nb-PRG的多(n)尺寸,深度为d的电路C:{0,1}〜n→{0,1}〜t,其中r = O(l log〜 (d + O(1)n)。这是对杜布罗夫(Dubrov)和伊斯海(Ishai)先前的工作进行了改进,其中r≥t〜2。通过将Trevisan和Xue [2013]的最近PRG结构修改为nb- PRG。我们还表明,该PRG可以由统一的恒定深度电路系列实现,种子长度略有增加。

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