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A pseudo-random number generator PRNG is a finite state machine with an initial value called the seed .
Upon each request, a transaction function computes the next internal state and an output function produces the actual number based on the state.
A PRNG deterministically produces a periodic sequence of values that depends only on the initial seed given. An example would be a linear congruential generator like PM Thus, knowing even a short sequence of generated values it is possible to figure out the seed that was used and thus - know the next value.
However, assuming the generator was seeded with sufficient entropy and the algorithms have the needed properties, such generators will not quickly reveal significant amounts of their internal state, meaning that you would need a huge amount of output before you can mount a successful attack on them.
A hardware RNG is based on unpredictable physical phenomenon, referred to as "entropy source". Radioactive decay , or more precisely the points in time at which a radioactive source decays is a phenomenon as close to randomness as we know, while decaying particles are easy to detect.
Another example is heat variation - some Intel CPUs have a detector for thermal noise in the silicon of the chip that outputs random numbers.
Hardware RNGs are, however, often biased and, more importantly, limited in their capacity to generate sufficient entropy in practical spans of time, due to the low variability of the natural phenomenon sampled.
When the entropy is sufficient, it behaves as a TRNG. If you'd like to cite this online calculator resource and information as provided on the page, you can use the following citation: Georgiev G.
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