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A high quality random number generation (RNG) process is almost always required for security, and lack of quality generally provides attack vulnerabilities and so leads to lack of security, even to complete compromise, in cryptographic systems. [1] The RNG process is particularly attractive to attackers because it is typically a single isolated ...
That is, given the first k bits of a random sequence, there is no polynomial-time algorithm that can predict the (k+1)th bit with probability of success non-negligibly better than 50%. [1] Andrew Yao proved in 1982 that a generator passing the next-bit test will pass all other polynomial-time statistical tests for randomness. [2]
More specifically, in such a case, the order of any element divides p − 1, and there are only four possible divisors: 1, 2, ab r /2 − 1, or ab r − 2. The first two apply only to the elements 1 and −1, and quadratic reciprocity arguments show that the fourth option cannot apply to b , so only the third option remains.
The RNG validation list carries the following notice: "As of January 1, 2016, in accordance with the SP800-131A Revision 1 Transitions: Recommendation for Transitioning the Use of Cryptographic Algorithms and Key Lengths, the use of RNGs specified in FIPS 186-2, [X9.31], and the 1998 version of [X9.62] is no longer approved.
The Mersenne Twister has a period of 2 19 937 − 1 iterations (≈ 4.3 × 10 6001), is proven to be equidistributed in (up to) 623 dimensions (for 32-bit values), and at the time of its introduction was running faster than other statistically reasonable generators.
ISAAC (indirection, shift, accumulate, add, and count) is a cryptographically secure pseudorandom number generator and a stream cipher designed by Robert J. Jenkins Jr. in 1993. [1] The reference implementation source code was dedicated to the public domain. [2] "I developed (...) tests to break a generator, and I developed the generator to ...
Widely used in many programs, e.g. it is used in Excel 2003 and later versions for the Excel function RAND [8] and it was the default generator in the language Python up to version 2.2. [9] Rule 30: 1983 S. Wolfram [10] Based on cellular automata. Inversive congruential generator (ICG) 1986 J. Eichenauer and J. Lehn [11] Blum Blum Shub: 1986
There are several "pools" of entropy; each entropy source distributes its alleged entropy evenly over the pools; and (here is the key idea) on the nth reseeding of the generator, pool k is used only if n is a multiple of 2 k. Thus, the kth pool is used only 1/2 k of the time. Higher-numbered pools, in other words, (1) contribute to reseedings ...