Simple Affine Extractors Using Dimension Expansion

Matt DeVos, Ariel Gabizon
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引用次数: 23

Abstract

Let $\F$ be the field of $q$ elements. An \emph{\afsext{n}{k}} is a mapping $D:\F^n\ar\B$ such that for any $k$-dimensional affine subspace $X\subseteq \F^n$, $D(x)$ is an almost unbiased bit when $x$ is chosen uniformly from $X$. Loosely speaking, the problem of explicitly constructing affine extractors gets harder as $q$ gets smaller and easier as $k$ gets larger. This is reflected in previous results: When $q$ is `large enough', specifically $q= \Omega(n^2)$, Gabizon and Raz \cite{GR05} construct affine extractors for any $k\geq 1$. In the `hardest case', i.e. when $q=2$, Bourgain \cite{Bour05} constructs affine extractors for $k\geq \delta n$ for any constant (and even slightly sub-constant) $\delta>0$. Our main result is the following: Fix any $k\geq 2$ and let $d = 5n/k$. Then whenever $q>2\cdot d^2$ and $p=char(\F)>d$, we give an explicit \afsext{n}{k}. For example, when $k=\delta n$ for constant $\delta>0$, we get an extractor for a field of constant size $\Omega(\left(\frac{1}{\delta}\right)^2)$. We also get weaker results for fields of arbitrary characteristic (but can still work with a constant field size when $k=\delta n $ for constant $\delta > 0$). Thus our result may be viewed as a `field-size/dimension' tradeoff for affine extractors. For a wide range of $k$ this gives a new result, but even for large $k$ where we do not improve (or even match) the previous result of \cite{Bour05}, we believe that our construction and proof have the advantage of being very simple: Assume $n$ is prime and $d$ is odd, and fix any non-trivial linear map $T:\F^n\mapsto \F$. Define $QR:\F\mapsto \B$ by $QR(x)=1$ if and only if $x$ is a quadratic residue. Then, the function $D:\F^n\mapsto \B$ defined by $D(x)\triangleq QR(T(x^d))$ is an \afsext{n}{k}. Our proof uses a result of Heur, Leung and Xiang \cite{HLX02} giving a lower bound on the dimension of products of subspaces.
使用维度展开的简单仿射提取器
设$\F$为$q$元素的字段。\emph{\afsext{n}{k}}是一个映射$D:\F^n\ar\B$,对于任何$k$维仿射子空间$X\subseteq \F^n$,当$x$从$X$中均匀选择时,$D(x)$是一个几乎无偏的位。粗略地说,随着$q$变小,显式构造仿射提取器的问题变得越来越难,而随着$k$变大,显式构造仿射提取器变得越来越容易。这反映在之前的结果中:当$q$“足够大”时,特别是$q= \Omega(n^2)$, Gabizon和Raz \cite{GR05}为任何$k\geq 1$构建仿射提取器。在“最困难的情况下”,即$q=2$时,Bourgain \cite{Bour05}为$k\geq \delta n$构建仿射提取器,用于任何常数(甚至稍微次常数)$\delta>0$。我们的主要结果如下:修复任何$k\geq 2$并让$d = 5n/k$。然后每当$q>2\cdot d^2$和$p=char(\F)>d$时,我们给出一个显式的\afsext{n}{k}。例如,当$k=\delta n$表示常量$\delta>0$时,我们得到一个用于常量大小$\Omega(\left(\frac{1}{\delta}\right)^2)$字段的提取器。对于任意特征的字段,我们也得到较弱的结果(但当$k=\delta n $用于恒定$\delta > 0$时,仍然可以使用恒定的字段大小)。因此,我们的结果可以看作是仿射提取器的“字段大小/维度”权衡。对于较大范围的$k$,这给出了一个新的结果,但即使对于较大的$k$,我们也没有改进(甚至匹配)以前的\cite{Bour05}的结果,我们相信我们的构造和证明具有非常简单的优点:假设$n$是素数,$d$是奇数,并修复任何非平凡的线性映射$T:\F^n\mapsto \F$。当且仅当$x$是二次余数时,用$QR(x)=1$定义$QR:\F\mapsto \B$。那么,$D(x)\triangleq QR(T(x^d))$定义的函数$D:\F^n\mapsto \B$就是一个\afsext{n}{k}。我们的证明使用了Heur, Leung和Xiang的结果\cite{HLX02}给出了子空间积维数的下界。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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