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Computing Individual Discrete Logarithms Faster in GF(p~n) with the NFS-DL Algorithm

机译:使用NFS-DL算法计算单个离散对数速度(p〜n)的速度

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The Number Field Sieve (NFS) algorithm is the best known method to compute discrete logarithms (DL) in finite fields F_(p~n), with p medium to large and n ≥ 1 small. This algorithm comprises four steps: polynomial selection, relation collection, linear algebra and finally, individual logarithm computation. The first step outputs two polynomials defining two number fields, and a map from the polynomial ring over the integers modulo each of these polynomials to F_(p~n). After the relation collection and linear algebra phases, the (virtual) logarithm of a subset of elements in each number field is known. Given the target element in F_(p~n), the fourth step computes a preimage in one number field. If one can write the target preimage as a product of elements of known (virtual) logarithm, then one can deduce the discrete logarithm of the target. As recently shown by the Logjam attack, this final step can be critical when it can be computed very quickly. But we realized that computing an individual DL is much slower in medium- and large-characteristic non-prime fields F_(p~n) with n ≥ 3, compared to prime fields and quadratic fields F_(p~2). We optimize the first part of individual DL: the booting step, by reducing dramatically the size of the preimage norm. Its smoothness probability is higher, hence the running-time of the booting step is much improved. Our method is very efficient for small extension fields with 2 ≤ n ≤ 6 and applies to any n > 1, in medium and large characteristic.
机译:数字字段筛子(NFS)算法是最熟知的方法,用于计算有限字段F_(p〜n)中的离散对数(DL),P培养基为大,n≥1小。该算法包括四个步骤:多项式选择,关系收集,线性代数,最后,单个对数计算。所述第一步骤输出两个多项式限定两个数量的字段,并从多项式环的地图上的整数模每个多项式到F_(P〜n)中。在关系集合和线性代数阶段之后,每个数字字段中元素子集的(虚拟)对数是已知的。鉴于F_目标元件(P〜n)时,所述第四步骤中计算一个数字字段原像。如果可以将目标预报写为已知(虚拟)对数的元素的乘积,则可以推断目标的离散对数。正如Logjam攻击最近所示,当可以非常快速地计算时,最终步骤可能是至关重要的。但是,我们认识到,计算单个DL是具有n≥3在中期慢得多和大特性非黄金字段F_(P〜n)的,相对于素数域和二次域F_(P〜2)。我们优化单个DL:启动步骤的第一部分,通过急剧地减少预测范围的大小。其平滑概率较高,因此引导步骤的运行时间得到了很大改善。我们的方法是对小的扩展字段有2≤N≤6非常有效的,并适用于任意的n> 1时,在中型和大型特性。

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