...
首页> 外文期刊>Theory of Computing >Extractors for Polynomial Sources over Fields of Constant Order and Small Characteristic
【24h】

Extractors for Polynomial Sources over Fields of Constant Order and Small Characteristic

机译:常数级和小特征场上的多项式源提取器

获取原文
   

获取外文期刊封面封底 >>

       

摘要

A polynomial source of randomness over $F^n$ is a random variable $X=f(Z)$ where $f$ is a polynomial map and $Z$ is a random variable distributed uniformly over $F^r$ for some integer $r$. The three main parameters of interest associated with a polynomial source are the order $q$ of the field, the (total) degree $D$ of the map $f$, and the base-$q$ logarithm of the size of the range of $f$ over inputs in $F^r$, denoted by $k$. For simplicity we call $X$ a $(q,D,k)$-source. Informally, an extractor for $(q,D,k)$-sources is a function $E:F^no {0,1}^m$ such that the distribution of the random variable $E(X)$ is close to uniform over ${0,1}^m$ for any $(q,D,k)$-source $X$. Generally speaking, the problem of constructing extractors for such sources becomes harder as $q$ and $k$ decrease and as $D$ increases. A rather large number of recent works in the area of derandomization have dealt with the problem of constructing extractors for $(q,1,k)$-sources, also known as “affine” sources. Constructing an extractor for non-affine sources, i.e., for $D>1$, is a much harder problem. Prior to the present work, only one construction was known, and that construction works only for fields of order much larger than $n$ (Dvir et al., CCC 2009). In particular, even for $D=2$, no construction was known for any fixed finite field. In this work we construct extractors for $(q,D,k)$-sources for fields of constant order. Our proof builds on the work of DeVos and Gabizon (CCC 2010) on extractors for affine sources. Like the DeVos--Gabizon paper, our result makes crucial use of a theorem of Hou, Leung and Xiang (J. Number Theory 2002) which gives a lower bound on the dimension of products of subspaces. A conference version of this paper appeared in theProceedings of the 16th Internat. Workshop on Randomizationand Computation (RANDOM'12).
机译:$ F ^ n $上的多项式随机源是一个随机变量$ X = f(Z)$,其中$ f $是一个多项式映射,而$ Z $是一个均匀分布在$ F ^ r $上的随机变量一些整数$ r $。与多项式源相关的三个主要感兴趣的参数是字段的顺序$ q $,映射$ f $的(总)度$ D $和范围大小的底数$ q $对数$ f $超过$ F ^ r $中的输入值,以$ k $表示。为简单起见,我们将$ X $称为$(q,D,k)$源。非正式地,$(q,D,k)$源的提取器是函数$ E: F ^ n to {0,1 } ^ m $使得随机变量$ E(X )对于任何$(q,D,k)$来源$ X $,在$ {0,1 } ^ m $上均接近于统一。一般而言,随着$ q $和$ k $的减少以及$ D $的增加,为此类源构造提取器的问题变得更加困难。在去随机化领域,大量的近期工作解决了为$(q,1,k)$源(也称为“仿射”源)构造提取器的问题。为非仿射源(即$ D> 1 $)构建提取器是一个困难得多的问题。在目前的工作之前,只有一种构造是已知的,并且该构造仅在大于n $的有序字段上起作用(Dvir等人,CCC 2009)。特别是,即使对于$ D = 2 $,对于任何固定的有限域也没有构造。在这项工作中,我们为$(q,D,k)$来源构造常数阶字段的提取器。我们的证明建立在DeVos和Gabizon(CCC 2010)在仿射源提取器上的工作基础上。像DeVos-Gabizon论文一样,我们的结果关键地使用了Hou,Leung和Xiang的一个定理(J. Number Theory 2002),它给出了子空间乘积维数的下界。该论文的会议版本出现在第16届国际会议论文集中。随机化和计算研讨会(RANDOM'12)。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号