Thanks On Tue, Dec 6, 2022 at 10:39 AM Wes Turner <wes.tur...@gmail.com> wrote:
> > google/paranoid_crypto has a number of Randomness Tests: > >> google/paranoid_crypto has a number of Randomness Tests in Python IIR >> From grep '^#' >> https://github.com/google/paranoid_crypto/blob/main/docs/randomness_tests.md >> : >> >> ```md >> # Randomness tests >> ## Goal of the tests >> ## Non-goals >> ## Usage >> ## Tests >> ### NIST SP 800-22 >> #### Frequency (Monobits) Test >> #### Frequency Test within a Block >> #### Runs Test >> #### Test for the Longest Run of Ones in a Block >> #### Binary Matrix Rank Test >> #### Discrete Fourier Transform (Spectral) Test >> #### Non-Overlapping Template Matching Test >> #### Overlapping Template Matching Test. >> #### Maurer’s “Universal Statistical” Test >> #### Linear Complexity Test >> #### Serial Test. >> #### Approximate Entropy Test >> #### Cumulative Sums (Cusum) Test. >> #### Random Excursions Test. >> #### Random Excursions Variant Test >> ### Additional tests >> #### FindBias >> #### LargeBinaryMatrixRank >> #### LinearComplexityScatter >> ## Interface >> ### Repeating tests >> ## Testing >> ### Pseudorandom number generators for testing >> #### urandom >> #### mt19937 >> #### gmp_n >> #### mwc_n >> #### java >> #### lcgnist >> #### xorshift128+ >> #### xorshift* >> #### xorwow >> #### pcg64, philox, sfc64 >> #### jsf32, jsf64 >> ## Design decisions >> ``` >> >> $ grep '^\s*class' >> https://github.com/google/paranoid_crypto/blob/main/paranoid_crypto/lib/randomness_tests/rng.py >> : >> >> ```python >> class Rng: >> class Urandom(Rng): >> class Shake128(Rng): >> class Mt19937(Rng): >> class GmpRand(Rng): >> class XorShift128plus(Rng): >> class XorShiftStar(Rng): >> class Xorwow(Rng): >> class JavaRandom(Rng): >> class LcgNist(Rng): >> class Mwc(Rng): >> class NumpyRng(Rng): >> class Lehmer(Rng): >> class Pcg64(NumpyRng): >> class Philox(NumpyRng): >> class Sfc64(NumpyRng): >> class SubsetSum(Rng): >> ``` >> >> From >> https://github.com/google/paranoid_crypto/blob/16e5f47fcc11f51d3fb58b50adddd075f4373bbc/paranoid_crypto/lib/randomness_tests/random_test_suite.py#L42-L80 >> : >> >> ```python >> NIST_TESTS = [ >> (nist_suite.Frequency, []), >> (nist_suite.BlockFrequency, []), >> (nist_suite.Runs, []), >> (nist_suite.LongestRuns, []), >> (nist_suite.BinaryMatrixRank, []), >> (nist_suite.Spectral, []), >> (nist_suite.NonOverlappingTemplateMatching, []), >> (nist_suite.OverlappingTemplateMatching, []), >> (nist_suite.Universal, []), >> (nist_suite.LinearComplexity, [512]), >> (nist_suite.LinearComplexity, [1024]), >> (nist_suite.LinearComplexity, [2048]), >> (nist_suite.LinearComplexity, [4096]), >> (nist_suite.Serial, []), >> (nist_suite.ApproximateEntropy, []), >> (nist_suite.RandomWalk, []), >> ] >> >> >> EXTENDED_NIST_TESTS = [ >> (extended_nist_suite.LargeBinaryMatrixRank, []), >> # Computing the linear complexity has quadratic complexity. >> # A consequence of this is that LinearComplexityScatter only >> # uses a fraction of the input. A parameter [n, m] means >> # that n m-bit sequences are tested, where the i-th sequence >> # consists of the bits i, i + n, ..., i + (m-1) * m. >> (extended_nist_suite.LinearComplexityScatter, [32, 100000]), >> (extended_nist_suite.LinearComplexityScatter, [64, 50000]), >> (extended_nist_suite.LinearComplexityScatter, [128, 40000]), >> ] >> >> >> LATTICE_TESTS = [ >> (lattice_suite.FindBias, [256]), >> (lattice_suite.FindBias, [384]), >> (lattice_suite.FindBias, [512]), >> (lattice_suite.FindBias, [1024]), >> ] >> >> >> TESTS = NIST_TESTS + EXTENDED_NIST_TESTS + LATTICE_TESTS >> ``` >> >> >> - [ ] ENH: paranoid_crypto: add a __main__ so that python -m >> paranoid_crypto.randomness_tests calls eg: >> - [x] >> https://github.com/google/paranoid_crypto/blob/main/examples/randomness.py >> - [ ] REF: examples/randomness.py -> lib/randomness_tests/main.py >> - [ ] ENH: paranoid_crypto.randomness_tests: add a __main__ so that >> `python -m paranoid_crypto.randomness_tests -h` works >> - [ ] ENH: setup.py: console_scripts entrypoint for >> examples/randomness_tests/main.py >> >> - [ ] DOC: >> https://en.wikipedia.org/wiki/Randomness_test#Notable_software_implementations: >> link to google/paranoid_crypto >> >> >> >> On Tue, Nov 15, 2022 at 7:25 AM Chris Angelico <ros...@gmail.com> wrote: >> >>> On Tue, 15 Nov 2022 at 22:41, Chris Angelico <ros...@gmail.com> wrote: >>> > >>> > (I'm assuming that you sent this personally by mistake, and am >>> > redirecting back to the list. My apologies if you specifically didn't >>> > want this to be public.) >>> > >>> > On Tue, 15 Nov 2022 at 22:34, James Johnson <jj126...@gmail.com> >>> wrote: >>> > > >>> > > It’s been a couple of years ago, but as I recall the duplicates >>> seemed to be of two or three responses, not randomly distributed. >>> > > >>> > > I looked at my code, and I DID salt the hash at every update. >>> > > >>> > > At this point, my curiosity is engaged to know if this s/w solution >>> is as good as others. I don’t have the training to test how often 9 follows >>> 5, for example, but I am competitive enough to ask how it holds up against >>> MTprng. I think it’s possibly very good, for s/w, and I’m emboldened to ask >>> you to modify the code (it requires you data enter the numbers back to the >>> machine, allowing time to pass;) to accumulate the results for 2 or 3 >>> million, and see how it holds up. I don’t think the numbers track the bell >>> curve on distribution . I speculate it’s more square. I suppose this is >>> desirable in a PRNG? >>> > > >>> > >>> > I'll get you to do the first step of the modification. Turn your code >>> > into a module that has a randbelow() function which will return a >>> > random integer from 0 up to the provided argument. (This is equivalent >>> > to the standard library's random.randrange() function when given just >>> > one argument.) If you like, provide several of them, as randbelow1, >>> > randbelow2, etc. >>> > >>> > Post that code, and then I'll post a test harness that can do some >>> > analysis for you. >>> >>> Here's a simple test harness. There are other tests you could use, but >>> this one is pretty straight-forward. >>> >>> https://github.com/Rosuav/shed/blob/master/howrandom.py >>> >>> For each test, it counts up how many times each possible sequence >>> shows up, then displays the most and least common, rating them >>> according to how close they came to a theoretical perfect >>> distribution. A good random number generator should produce results >>> that are close to 100% for all these tests, but the definition of >>> "close" depends on the pool size used (larger means closer, but also >>> means more CPU time) and the level of analysis done. In my testing, >>> all of the coin-flip data showed values +/- 1%, and the others never >>> got beyond 10%. >>> >>> This is the same kind of analysis that was used in the page that I >>> linked to earlier, so you can play with it interactively there if >>> you're curious. It also has better explanations than I would give. >>> >>> Again, there are plenty of other types of tests you could use, but >>> this is a pretty easy one. True randomness should show no biases in >>> any of these results, though there will always be some variance. >>> >>> ChrisA >>> _______________________________________________ >>> Python-ideas mailing list -- python-ideas@python.org >>> To unsubscribe send an email to python-ideas-le...@python.org >>> https://mail.python.org/mailman3/lists/python-ideas.python.org/ >>> Message archived at >>> https://mail.python.org/archives/list/python-ideas@python.org/message/5OWDW5XUE5JYAW3QKMNZYKXH3NNBPNNW/ >>> Code of Conduct: http://python.org/psf/codeofconduct/ >>> >> _______________________________________________ > Python-ideas mailing list -- python-ideas@python.org > To unsubscribe send an email to python-ideas-le...@python.org > https://mail.python.org/mailman3/lists/python-ideas.python.org/ > Message archived at > https://mail.python.org/archives/list/python-ideas@python.org/message/DCM552CL5I2SV47GTU4CUYLONTXWA6AG/ > Code of Conduct: http://python.org/psf/codeofconduct/ >
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