Difference between TRNG or PRNG?

Dear Korakot Boonyaphon,

The difference between true random number generators(TRNGs) and pseudo-random number generators(PRNGs) is that TRNGs use an unpredictable physical means to generate numbers (like atmospheric noise), and PRNGs use mathematical algorithms (completely computer-generated).

Pseudo-RNG's are sets of algorithms (i.e. logical instructions for by a computer program) that utilize mathematical formulas to produce a certain sequence of numbers that will appear random, or at least will have the effect of randomness. Other RNG's use pre-calculated tables instead of mathematical formulas, but either way table or formula, the objective is still the same.

A lot of research has been done (and is still ongoing) on RNG's insomuch that our modern algorithms used for Pseudo-RNG's produce number sequences that do exactly mimic truly random sequences. But that may be the outcome of today's research there is still room for improvement since the results of Pseudo-RNG's are really "pseudo".

Comparing the results of a Pseudo-RNG with that from a True-RNG will prove useful. If you list down the results of a Pseudo-RNG mimicking dice rolls the numbers will really appear as if they are random. But statistical analysis will prove that the numbers produced by a Pseudo-RNG is not really random but is rather predetermined. Thus its results can be measured and standardized, and, we can say, controlled.

True-RNG's behave differently since the results are truly random and unpredictable. If we try to get a computer to produce a really random sequence of numbers then the computer must base its numbers on a naturally occurring physical phenomenon, which may include the radioactive decay of isotopes, static in the airwaves, and the waves of the sea. True-RNG's will not really be cost-efficient if you compare it to Pseudo-RNG's. True-RNG's will be subject to wear and tear since all things natural will really be subject to entropy. A Pseudo-RNG will not be subject to such physical phenomena.

Another efficiency of a Pseudo-RNG is that you can reproduce the same sequence of numbers in another time by simply knowing the starting point of the sequence (thus it is also periodic and the sequence will repeat itself in due time). This better facilitates inspections done by casino control boards. But you can be sure that the sequence of numbers produced by a Pseudo-RNG, though periodic, the time to reproduce the same sequence will eventually take a really long time that unless you acquire the actual algorithms, the correct sequence will be very difficult (almost impossible) to crack.

Many online casinos have settled for Pseudo-RNG's for its cost-efficiency, periodicity, and efficiency. These are good reasons to choose this type of random number generator, though a True-RNG will also have its own benefits as well.

I hope I have answered your question.

With Best Wishes,

Samir G. Pandya

Dear Korakot,

The difference between the true random number generator and the pseudo random number generator is that the TRNG uses unpredictable physical means to generate numbers such as atmospheric, photospheric, cosmological noise ... while the PRNG uses mathematical algorithms that are fully computer generated.

For more details and information about this subject, i suggest you to take a look at attached files on topic.

Best regards

pseudo random number generates a random number which is predicted by using a mathematical formula usually computer programs are pseudo random..

The randomness comes from the atmospheric noise,is referred as true random number generator which is better than the pseudo random number generator.

Dear Korakot Boonyaphon, The two fundamental criteria to evaluate in the generator are: 1- The generated sequence is random ?.  Answering this question is not as simple a task as it seems, but there are numerous statistical tests of randomness that can tell you with confidence when it is not random. If your generator is for cryptographic applications, it must also be unpredictable. 2- Is the output of your generator reproducible? This is only possible if it was generated with some mathematical algorithm. If you answered both questions positively, your generator is a PRNG. If the output can be considered random but not reproducible, because it was obtained from physical elements, like what you mention then it is a TRNG. If the output of your generator does not meet the randomness tests, your generator is not even random.

 The choice of the minimum set of independent randomness tests that evaluate the greatest possible number of characteristics or patterns in succession is a research problem that deserves the most attention.

Best regards C.M.legon

In my point of view, there is a difference between PRNG and TRNG. TRNG or RNG is a random generator that generate a truly random sequence that uses one or some (combined) entropy sources, so that can not be controlled by people. In other words, the generation is conducted in non-deterministic way because only triggered by the entropy sources. The entropy sources used can based on hardware (noise, temperature, thermal, movement, etc) or based on software (mouse movements, key strokes, I/O buffer, System clock, file access, etc). Because the random sequences are generated in non deterministic way, then it can not be reproduced. Meanwhile the PRNG is a generator that produces pseudorandom sequences in deterministic way, because already based on a certain mathematical formulation. In the application, this type needs an input string as a seed with quietly short length such as 128 bits. For security sake it is recommended to use a random string. For detail you can find in books of Encyclopedia of Cryptography and Security by Henk C.A. van Tilborg or Cryptography Engineering by Bruce Schneier, et al.

Samir G Pandya , samir mai friend good point ;)

good day! can some one please assist me with "

Pseudo and Real Random Number Generators Testing"

Dear Yusuf yau Ali

I recommend you consultar

"Statistical testing off cryptographic Randomness", en

Dear yusuf

The above tests measure only Randomness of output, necessary condition, but not enough

To evaluate the independence between the, outputs and inputs, of the algorithm, I recommend you consult :

Hi,

Please check those links and attached files on subject.

Best regards

thank you all for your fit back, it really helped a lot