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Enhanced pseudorandom number generator based on Blum-Blum-Shub and elliptic curves

机译:基于Blum-Blum-Shub和椭圆曲线的增强型伪随机数生成器

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Blum-Blum-Shub (BBS) is a less complex pseudorandom number generator (PRNG) that requires very large modulus and a squaring operation for the generation of each bit, which makes it computationally heavy and slow. On the other hand, the concept of elliptic curve (EC) point operations has been extended to PRNGs that prove to have good randomness properties and reduced latency, but exhibit dependence on the secrecy of point P. Given these pros and cons, this paper proposes a new BBS-ECPRNG approach such that the modulus is the product of two elliptic curve points, both primes of length, and the number of bits extracted per iteration is by binary fraction. We evaluate the algorithm performance by generating 1000 distinct sequences of 106bits each. The results were analyzed based on the overall performance of the sequences using the NIST standard statistical test suite. The average performance of the sequences was observed to be above the minimum confidence level of 99.7 percent and successfully passed all the statistical properties of randomness tests.
机译:Blum-Blum-Shub(BBS)是一种不太复杂的伪随机数生成器(PRNG),它需要非常大的模数和平方运算才能生成每个位,这使其计算量大且缓慢。另一方面,椭圆曲线(EC)点操作的概念已扩展到PRNG,这些PRNG被证明具有良好的随机性并减少了等待时间,但表现出对P点保密性的依赖性。鉴于这些优点和缺点,本文提出了一种新的BBS-ECPRNG方法,使得模量是两个椭圆曲线点(均为长度素数)的乘积,并且每次迭代提取的位数是二进制分数。我们通过生成10个不同的1000个序列来评估算法性能 6 每个位。使用NIST标准统计测试套件,基于序列的整体性能对结果进行了分析。观察到序列的平均性能高于99.7%的最低置信度,并成功通过了随机性检验的所有统计特性。

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