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引用次数: 18
摘要
在本文中,我们重点研究了函数域筛(Function Field Sieve, FFS)的关系收集步骤,这是迄今为止已知的在密码大小的小特征有限域中计算离散对数的最佳算法。用Fpn表示这样一个有限域,其中p比n小得多,这一步背后的主要思想是在Fp[t][x]中找到形式为a(t)-b(t)x的多项式,当这些多项式被认为是精心选择的函数域中的主理想时,可以分解成低次素理想的乘积。这样的多项式被称为“关系”,而当前创纪录规模的离散对数计算需要数十亿个这样的多项式。因此,收集关系在FFS中是一个关键且极其昂贵的步骤,其实际实现需要大量使用缓存感知筛选算法,以及对Fp[t]的高效多项式算法。本文介绍了作为FFS新公共实现的一部分的算法和算术技术,旨在进行中等到记录大小的计算。
In this paper, we focus on the relation collection step of the Function Field Sieve (FFS), which is to date the best algorithm known for computing discrete logarithms in small-characteristic finite fields of cryptographic sizes. Denoting such a finite field by Fpn, where p is much smaller than n, the main idea behind this step is to find polynomials of the form a(t)-b(t)x in Fp[t][x] which, when considered as principal ideals in carefully selected function fields, can be factored into products of low-degree prime ideals. Such polynomials are called "relations", and current record-sized discrete-logarithm computations need billions of those. Collecting relations is therefore a crucial and extremely expensive step in FFS, and a practical implementation thereof requires heavy use of cache-aware sieving algorithms, along with efficient polynomial arithmetic over Fp[t]. This paper presents the algorithmic and arithmetic techniques which were put together as part of a new public implementation of FFS, aimed at medium-to record-sized computations.