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首页> 外文期刊>IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences >Reduction Optimal Trinomials for Efficient Software Implementation of the η_t Pairing
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Reduction Optimal Trinomials for Efficient Software Implementation of the η_t Pairing

机译:简化的最佳三项式用于η_t配对的高效软件实现

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The η_T pairing for supersingular elliptic curves over GF(3~m) has been paid attention because of its computational efficiency. Since most computation parts of the η_T pairing are GF(3~m) multiplications, it is important to improve the speed of the multiplication when implementing the η_T pairing. In this paper we investigate software implementation of GF(3~m) multiplication and propose using irreducible trinomials x~m+ax~k+b over GF(3) such that k is a multiple of w, where w is the bit length of the word of targeted CPU. We call the trinomials "reduction optimal trinomials (ROTs)." ROTs actually exist for several m's and for typical values of w = 16 and 32. We list them for extension degrees m = 97, 167, 193, 239, 317, and 487. These m's are derived from security considerations. Using ROTs, we are able to implement efficient modulo operations (reductions) for GF(3~m) multiplication compared with cases in which other types of irreducible trinomials are used (e.g., trinomials with a minimum k for each m). The reason for this is that for cases using ROTs, the number of shift operations on multiple precision data is reduced to less than half compared with cases using other trinomials. Our implementation results show that programs of reduction specialized for ROTs are 20-30% faster on 32-bit CPU and approximately 40% faster on 16-bit CPU compared with programs using irreducible trinomials with general k.
机译:GF(3〜m)上奇异椭圆曲线的η_T配对由于其计算效率而受到关注。由于大多数η_T配对的计算部分都是GF(3〜m)乘法,因此实现η_T配对时提高乘法速度很重要。在本文中,我们研究了GF(3〜m)乘法的软件实现,并建议在GF(3)上使用不可约三项式x〜m + ax〜k + b,使得k为w的倍数,其中w为目标CPU一词。我们将三项式称为“还原最优三项式(ROT)”。 ROT实际上存在几个m,典型值w = 16和32。我们列出了它们的扩展度m = 97、167、193、239、317和487。这些m是出于安全考虑。与使用其他类型的不可约性三项式(例如,每m个最小k的三项式)相比,使用ROT,我们能够对GF(3〜m)乘以实现有效的模运算(归约)。原因是对于使用ROT的情况,与使用其他三项式的情况相比,对多个精度数据进行移位运算的次数减少到少于一半。我们的实施结果表明,与使用一般性k的不可约三项式的程序相比,专门用于ROT的缩减程序在32位CPU上快20-30%,在16位CPU上快40%。

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