I am an amateur enthusiast. The random function I found in documentation was in 2014 or so. I do not have the vocabulary to differentiate between various prng's. I only knew of one, and I it returned some version of the bell curve, with three favorite values, and one of THEM a pronounced preference.
The scholars here are referencing white papers and using hardware and software interchangeably (?) I am not a randomization expert, and I do not seek academic recognition. It's a novel hack is all, and I don't know how to invoke the quality algorithms you reference. Chris A. asked me to "post" a module. I'm pretty sure he means on git-hub, and I don't know how to use it. I attach a script with a randbelow() function. The kernel of my idea is to use randomness from a hash value, so I declared the hash globally, and called it in the function. I know this may not be correct, but I don't know how to declare one momentarily and then destruct it or release the ram the next minute, between random numbers. Normally, I would update the hexdigest outside the function, and pass it to randbelow(range, hexdigest_string) as two arguments. Chris A wants it to feed an analysis program that may not agree well with the "permanence" of a hash between random numbers. I am willing to put in effort, but it's a steep learning curve for me. I DO understand that my first script was a lot of data entry, data checking and elementary reporting that was not germane. I use my fast computer off-line, and connect to the web with a Chromebook, so online tools would mean changing my setup. I remain interested to help and learn. I hope this is not too bare-bones. On Tue, Nov 15, 2022 at 7:17 AM Stephen J. Turnbull < stephenjturnb...@gmail.com> wrote: > James Johnson writes: > > > I want to be good natured about it, not combative. I tried whatever > random > > function I found in documentation online, and used it. Without analyzing > > the world, I was simply dissatisfied with the results. > > On the Python lists, you'll get much better discussion if you're > specific about your "dissatisfaction". (If it's just the repetition > problem, that's better handled by composing with a filter than by > altering the PRNG itself, which should be as close to uniform as > possible to make generating other distributions as simple as possible.) > > Your method *as described* is unclear. Don Knuth tried something > similar (you have better tools available than he did, but the basic > idea seems to be similarly "let's compose a few pseudo-randomizing > transformations"), and got an embarrassing result. (Spoiler in > footnote [1] if you don't feel like digging through Seminumerical > Algorithms.) You may have had more method than Knuth did (cf Dave > Mertz's statement that it "looks like Random123"), but again, it would > really help communication if you described why you think your PRNG > will do better than Python's current one. It's a heavily researched > field for many decades now, and there are quite a few core devs with a > lot of knowledge and experience in the field (Python apps provide > quite of bit of the "attack surface" exposed on the Internet, it would > be irresponsible if there weren't!) > > By the way, nobody here is going to laugh if it turns out that your > thinking was naive. My point is to communicate more effectively, not > that your idea is bad (despite having a copy of Seminumerical > Algorithms, I'm not competent to evaluate your algorithm itself :-). > How you feel about "getting schooled" is a personal thing, but my > personal experience has been that there's an excellent education to be > had by posting naive ideas to this list. :-) > > Sincere regards, > > N.B. Deliberately not trimming so you have to work to see the > spoiler. :-) > > > > > Here’s what I settled on. I actually “put my thumb on the scales,” to > rule > > out repetitions for seven questions, so randomness didn’t ultimately > answer > > my question. > > > > I’m not sure that the function I “got” was the Mersenne Twister. I do > know > > Mersenne was a truly great mathematician. > > > > I acknowledge that updating with time.asctime() EVERY time I update the > > hash would be random; I’m not qualified to know if it would be “more” > > random. After you ask about the bell curve v square distributions, I am > > quite out of my depth. > > > > Here’s my “wizard” for anyone who wants to sell him cryptocurrency or > > overcome his objections to the environmentalist agenda, or query him > about > > foreign policy. I hope you find it better than average. > > > > https://drive.google.com/file/d/1EqQsMfBHDrNpOBQrI7CJxrpbowvKPD2G/ > > > > Regards, > > > > James J > > > > On Tue, Nov 15, 2022 at 4:31 AM Wes Turner <wes.tur...@gmail.com> > wrote: > > > > > While FWIU it is advisable to keep seeding an RNG after startup - > that's > > > what e.g. rngd does - the cryptography.io docs do advise to just use > > > `os.urandom()` (which is not the same as random.SystemRandom()?). > > > > > > There probably should be better default random in CPython; though I'm > > > personally not at all qualified to assess, TIL about NIST 800-22 and > FIPS > > > 140-2 for evaluating sources of entropy. > > > > > > Differential entropy > Differential entropies for various > distributions > > > https://en.wikipedia.org/wiki/Differential_entropy > > > > > > > > > *** > > > > > > (TIL that quantum information is never destroyed; so which physical > > > processes are actually nonreversible like hashing and RNG are > supposed to > > > be is up for consideration as classical information is a subset of > quantum > > > information. Do black holes shift gamma radiation may actually be > relevant! > > > Perhaps a permanent magnet under a (double-jointed?) bar and ball > pendulum > > > on a magnetic bearing would produce enough Brownian motion to get > enough > > > uniform random to call it sufficiently entropic for purposes of > CSPRNG? > > > Nondeterministic NDE fluid calculations diffract into many possible > > > outcomes, but a brute force combinatorial search of all the possible > return > > > values is still deterministically ordered. And the quantum computing > folks > > > are working on increasing coherence / reducing error propagation; > like ECC.) > > > > > > - [ ] DOC: The docs could advise regarding which decent enough open > source > > > hw RNG would be supported by os.urandom or random.SystemRandom if > rngd is > > > not configured to keep nondeterministically seeding with entropy that > > > should presumably be from a Uniform random entropic process > > > > > > There should be better software random in python. > > > > > > > > > On Tue, Nov 15, 2022, 1:19 AM James Johnson <jj126...@gmail.com> > wrote: > > > > > >> Thank you for replying with such specific assistance. I am made > acutely > > >> aware that I am only a Python enthusiast, and not an academic. > > >> > > >> Hashes are deterministic, not random, but byte by byte, they can be > very > > >> random. Please accept the attached script as a "hack," that might be > novel, > > >> or a curiosity. My first script was defective several ways. In this, > I seed > > >> the hash by hashing the uu-8 ENCODED string value of the time - it > is not > > >> random, but it is rarely the same twice. I used 0-9 because it is > decimal, > > >> and 0-59, because hours and minutes come in 60's. > > >> > > >> I found that taking time.time_nc() gave me far more odds than evens. > > >> > > >> I do not know how the scheme I devised of adding hex digits together > > >> would scale to larger ranges - it is done by adding hex digits that > vary > > >> from 0 - 15 repeatedly, but, since the number of hex digits varies > with the > > >> range, it could actually work for larger ranges. > > >> > > >> The random function in Python is not really adequate for a magic > eight > > >> ball program, so as a consumer I am dissatisfied without examining > it at a > > >> quantum level, for repeated reference thousands of times each. > > >> > > >> Thank you for your kind attention, and I hope the (much improved) > hack > > >> can be helpful to other enthusiasts. > > >> > > >> James J > > >> (A.A. Faulkner University) > > >> > > >> On Mon, Nov 14, 2022 at 11:23 AM Wes Turner <wes.tur...@gmail.com> > wrote: > > >> > > >>> QRNG "Quantum Random Number Generation" -> Hardware random number > > >>> generator > Physical phenomena with random properties > Quantum > random > > >>> properties > > >>> > > >>> > https://en.wikipedia.org/wiki/Hardware_random_number_generator#Quantum_random_properties > > >>> > > >>> > > >>> FWIW, SciPy and SymPy have various non-CSPRNG random distributions: > > >>> > > >>> - https://docs.scipy.org/doc/scipy/reference/stats.html > > >>> - > > >>> > https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.uniform.html#scipy.stats.uniform > > >>> > > >>> - https://docs.sympy.org/latest/modules/stats.html > > >>> - > https://docs.sympy.org/latest/modules/stats.html#sympy.stats.Uniform > > >>> - > > >>> > https://docs.sympy.org/latest/modules/stats.html#sympy.stats.DiscreteUniform > > >>> > > >>> "RFC 8937: Randomness Improvements for Security Protocols" (2020) > > >>> https://www.rfc-editor.org/rfc/rfc8937.html > > >>> > > >>> *** > > >>> > > >>> FWIW, > > >>> UUIDs and W3C DIDs require entropy e.g. from a CSPRNG or better: > > >>> https://www.w3.org/TR/did-core/#terminology : > > >>> > > >>> > Universally Unique Identifier (UUID) > > >>> > A type of globally unique identifier defined by [RFC4122]. UUIDs > are > > >>> similar to DIDs in that they do not require a centralized > registration > > >>> authority. UUIDs differ from DIDs in that they are not resolvable or > > >>> cryptographically-verifiable > > >>> > > >>> On Mon, Nov 14, 2022, 11:54 AM Wes Turner <wes.tur...@gmail.com> > wrote: > > >>> > > >>>> https://docs.python.org/3/library/random.html : > > >>>> > > >>>> > Warning: The pseudo-random generators of this module should not > be > > >>>> used for security purposes. For security or cryptographic uses, > see the > > >>>> secrets module > > >>>> > > >>>> https://docs.python.org/3/library/secrets.html#module-secrets > > >>>> > > >>>> PEP 506 – Adding A Secrets Module To The Standard Library > > >>>> https://peps.python.org/pep-0506/#alternatives > > >>>> https://github.com/python/peps/blob/main/pep-0506.txt > > >>>> > > >>>> PEP 12: new PEP template: > > >>>> https://github.com/python/peps/blob/main/pep-0012/pep-NNNN.rst > > >>>> > > >>>> Pseudorandom number generator > Cryptographic PRNGs > > >>>> > > >>>> > https://en.wikipedia.org/wiki/Pseudorandom_number_generator#Cryptographic_PRNGs > > >>>> > > >>>> Random number generator attack > Defenses > > >>>> > https://en.wikipedia.org/wiki/Random_number_generator_attack#Defenses > > >>>> > > >>>> > > >>>> /? CSPRNG > > >>>> https://www.google.com/search?q=CSPRNG > > >>>> > > >>>> From "THE LINUX CSPRNG IS NOW GOOD!" > > >>>> https://words.filippo.io/dispatches/linux-csprng/ : > > >>>> > > >>>> > [ get random() is from OpenBSD and LibreSSL ] > > >>>> > > >>>> > Performance and ChaCha20 > > >>>> > Some people would say they needed a userspace CSPRNG for > PERFORMANCE. > > >>>> I never really believed most of them, but to be fair Linux was > using a > > >>>> kinda slow SHA-1 extractor back then. However, since Linux 4.8 > (2016) the > > >>>> default getrandom(2) source is a fast ChaCha20-based CSPRNG, with > separate > > >>>> pools per NUMA node to avoid contention. (Just ignore the rude > comments in > > >>>> the code about applications not running their own CSPRNG, this is > still > > >>>> Linux after all.) > > >>>> > > > >>>> > There's even a neat trick XOR'ing some of the CSPRGN output back > into > > >>>> the ChaCha20 state to prevent an attacker from recovering any past > output > > >>>> from before the time of compromise. > > >>>> > > > >>>> > Some of these improvements came along thanks to the Wireguard > work by > > >>>> Jason A. Donenfeld > > >>>> > > >>>> "Problems emerge for a unified /dev/*random" (2022) > > >>>> https://lwn.net/Articles/889452/ > > >>>> > > >>>> From > > >>>> > https://www.redhat.com/en/blog/understanding-red-hat-enterprise-linux-random-number-generator-interface > > >>>> : > > >>>> > > >>>> """ > > >>>> How does the kernel initialize its CSPRNG? > > >>>> The kernel has an “entropy pool,” a place where unpredictable input > > >>>> observed by the kernel is mixed and stored. That pool serves as a > seed to > > >>>> the internal CSPRNG, and until some threshold of estimated entropy > is > > >>>> reached initially, it is considered uninitialized. > > >>>> > > >>>> Let’s now see how the kernel initializes its entropy pool. > > >>>> > > >>>> 1. After the kernel takes control on power-on, it starts filling > its > > >>>> entropy pool by mixing interrupt timing and other unpredictable > input. > > >>>> > > >>>> 2. The kernel gives control to systemd. > > >>>> > > >>>> 3. Next, systemd starts and initializes itself. > > >>>> > > >>>> 4. Systemd, optionally, loads kernel modules which will improve the > > >>>> kernel's entropy gathering process on a virtual machine (e.g., > virtio-rng). > > >>>> > > >>>> 5. Systemd loads the rngd.service which will gather additional > input > > >>>> entropy obtained via a random generator exposed by hardware (e.g., > the x86 > > >>>> RDRAND instruction or similar) and jitter entropy1; this entropy > is fed > > >>>> back into the kernel to initialize its entropy pool, typically in > a matter > > >>>> of milliseconds. > > >>>> > > >>>> After the last step, the kernel has its entropy pool initialized, > and > > >>>> any systemd services started can take advantage of the kernel’s > random > > >>>> generator. > > >>>> > > >>>> Note that the virtio-rng kernel module loading in step (3), is an > > >>>> optional step which improves entropy gathering in a virtual > machine by > > >>>> using the host's random generator to initialize the guest systems > in KVM. > > >>>> The rngd.service loading at the final step (4) is what ensures > that the > > >>>> kernel entropy pools are initialized on every scenario, and > furthermore it > > >>>> continues mixing additional data in the kernel pool during system > runtime. > > >>>> """ > > >>>> > > >>>> https://github.com/nhorman/rng-tools/blob/master/fips.c : > > >>>> > > >>>> ```c > > >>>> /* fips.c -- Performs FIPS 140-1/140-2 RNG tests > > >>>> ``` > > >>>> > > >>>> /? FIPS 140-1/140-2 RNG tests > > >>>> https://www.google.com/search?q=FIPS+140-1%2F140-2+RNG+tests > > >>>> > > >>>> /? CMVP "cprng" > > >>>> https://www.google.com/search?q=CMVP+%22cprng%22 > > >>>> https://csrc.nist.gov/publications/detail/fips/140/3/final > > >>>> > > >>>> https://www.google.com/search?q=rng+tests > > >>>> > > >>>> - https://www.johndcook.com/blog/rng-testing/ : > > >>>> > > >>>> > We test RNGs using the standard test suites: PractRand, TestU01 > > >>>> (BigCrush), DIEHARD(ER), NIST SP 800-22. > > >>>> > > >>>> Randomness tests: > > >>>> > > >>>> > https://en.wikipedia.org/wiki/Randomness_test#Notable_software_implementations > > >>>> : > > >>>> - https://en.wikipedia.org/wiki/Diehard_tests > > >>>> - https://en.wikipedia.org/wiki/TestU01 > > >>>> - /? NIST 800-22 https://www.google.com/search?q=nist+800-22 > > >>>> > > >>>> /? nist 800-22 site:github.com > > >>>> https://www.google.com/search?q=nist+800-22+site%3Agithub.com > > >>>> > > >>>> - > > >>>> > https://github.com/google/paranoid_crypto/blob/main/docs/randomness_tests.md > > >>>> > > >>>> > > >>>> From https://cryptography.io/en/latest/random-numbers/ > > >>>> > https://github.com/pyca/cryptography/blob/main/docs/random-numbers.rst > > >>>> : > > >>>> > > >>>> > > >>>> ```rst > > >>>> Random number generation > > >>>> ======================== > > >>>> > > >>>> When generating random data for use in cryptographic operations, > such > > >>>> as an > > >>>> initialization vector for encryption in > > >>>> :class:`~cryptography.hazmat.primitives.ciphers.modes.CBC` mode, > you do > > >>>> not > > >>>> want to use the standard :mod:`random` module APIs. This is because > > >>>> they do not > > >>>> provide a cryptographically secure random number generator, which > can > > >>>> result in > > >>>> major security issues depending on the algorithms in use. > > >>>> > > >>>> Therefore, it is our recommendation to `always use your operating > > >>>> system's > > >>>> provided random number generator`_, which is available as > > >>>> :func:`os.urandom`. > > >>>> For example, if you need 16 bytes of random data for an > initialization > > >>>> vector, > > >>>> you can obtain them with: > > >>>> > > >>>> .. doctest:: > > >>>> > > >>>> >>> import os > > >>>> >>> iv = os.urandom(16) > > >>>> > > >>>> This will use ``/dev/urandom`` on UNIX platforms, and > > >>>> ``CryptGenRandom`` on > > >>>> Windows. > > >>>> > > >>>> If you need your random number as an integer (for example, for > > >>>> :meth:`~cryptography.x509.CertificateBuilder.serial_number`), you > can > > >>>> use > > >>>> ``int.from_bytes`` to convert the result of ``os.urandom``: > > >>>> > > >>>> .. code-block:: pycon > > >>>> > > >>>> >>> serial = int.from_bytes(os.urandom(20), byteorder="big") > > >>>> > > >>>> In addition, the `Python standard library`_ includes the > ``secrets`` > > >>>> module, > > >>>> which can be used for generating cryptographically secure random > > >>>> numbers, with > > >>>> specific helpers for text-based formats. > > >>>> > > >>>> .. _`always use your operating system's provided random number > > >>>> generator`: > > >>>> > https://sockpuppet.org/blog/2014/02/25/safely-generate-random-numbers/ > > >>>> .. _`Python standard library`: > > >>>> https://docs.python.org/3/library/secrets.html > > >>>> > > >>>> ``` > > >>>> > > >>>> > > >>>> On Mon, Nov 14, 2022, 10:57 AM Barry <ba...@barrys-emacs.org> > wrote: > > >>>> > > >>>>> > > >>>>> > > >>>>> > On 14 Nov 2022, at 14:31, James Johnson <jj126...@gmail.com> > wrote: > > >>>>> > > > >>>>> > > > >>>>> > I wrote the attached python (3) code to improve on existing prng > > >>>>> functions. I used the time module for one method, which resulted > in > > >>>>> disproportionate odd values, but agreeable means. > > >>>>> > > > >>>>> > I used the hashlib module for the second. It is evident that the > > >>>>> code is amateur, but the program might result in better PRN > generation. > > >>>>> > > > >>>>> > The "app" lends itself to checking, using statistical tools (see > > >>>>> comments.) > > >>>>> > > >>>>> Have you used any cryptographic tools to prove the quality of your > > >>>>> PRNG? > > >>>>> What results did you get? > > >>>>> How does your PRNG compare to what python already has? > > >>>>> > > >>>>> Without that this analysis this will be unlikely to be considered > as a > > >>>>> candidate for python stdlib. > > >>>>> > > >>>>> Barry > > >>>>> > > >>>>> > > > >>>>> > I remain a fan, > > >>>>> > > > >>>>> > James Johnson > > >>>>> > <testrandom.py> > > >>>>> > _______________________________________________ > > >>>>> > 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/VENAT2YTVYVHTBSEVHHMIURCU6MG2CEO/ > > >>>>> > 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/HWQV4AKQAUM7CY4NWS2IRIVLRAYMKR3V/ > > >>>>> Code of Conduct: http://python.org/psf/codeofconduct/ > > >>>>> > > >>>> -- > > Truth causes consequences; consequences bring pain; pain exorcises > guilt! > > _______________________________________________ > > 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/ODL773AIV43GLUSO5UUNIPZYTPYJPQOZ/ > > Code of Conduct: http://python.org/psf/codeofconduct/ > > Footnotes: > [1] He composed several pseudo-random transformations, mostly linear > congruential but also including at least one nonlinear one (middle > square). The resulting sequence always converged to a short cycle > within a handful of iterations. > >
randbelow.py
Description: Binary data
_______________________________________________ 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/4BWHIOYYL6GCTKDRFKZB7MPZD6MMONJB/ Code of Conduct: http://python.org/psf/codeofconduct/
