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the result is truly unpredictable (unpredictable with respect to the computational capability of the attacker), which makes them much more difficult to predict or guess. yet, drngs work like the trng with respect to the unpredictability of individual values and in the overall distribution of generated values. that is, the drng's internal state is an unknown, but constant, function of the entropy source's state. once the drng is known, it is possible to predict values, but the predictions will not be reliable, and the drng's random number sequences will remain unpredictable.
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the drng achieves this high level of unpredictability in two ways. the first is that the hardware-implemented entropy source is itself unpredictable. the entropy source is made to extract entropy from a physical process and then use that entropy to generate unpredictability. the second, related, benefit is that the drng, unlike the trng, is based on a known, constant relationship between the entropy source and the generated numbers. that is, the drng is completely deterministic, and no part of its internal state can be inferred from examining its output. for example, an attacker cannot deduce the entropy source's internal state by analyzing a set of random numbers.
drngs are used to generate random bytes and typically have a much larger state than software-based prngs. in fact, drngs can produce many billion bytes of random numbers from a single seed value. this makes drngs ideal for applications that require a large volume of random numbers.
if the application is seeded by an external source, it is assumed that this source is capable of generating uniformly distributed pseudorandom values of sufficient quality. if the drng is seeded directly, and is used by the application to generate random values, it is assumed that the entropy source is of sufficient quality. in both cases, the prng is assumed to have a quality that is not be directly measurable. many systems have hardware random number generators and can provide a baseline quality for the prng. this allows for a transition from the initial seed to the prng. some systems may have a hardware rng or entropy source of variable quality. there are techniques to improve the quality of the entropy source, but they do not change the basic problem. the quality of a hardware random number generator is affected by hardware defects and internal thermal noise. this allows the state of a drng to be directly measurable and thus unusable for cryptographic functions.