In other words, by knowing the seed, it is possible to reconstruct the sequence of numbers produced by a particular PRNG. However, the generated random numbers from PRNG can be predictably traced back to the seed (initial states). The pseudo random number generator (PRNG) is another type of random number generator, also known as deterministic random bit generator (DRBG), used to produce keys. True random number generators (TRNGs) extract the dynamic entropy from random and microscopic fluctuations in physical processes (thermal noise, shot noise, avalanches, clock drift, jitter, atmospheric noise, external electromagnetics, quantum phenomena, etc.), which can generate independent, uniformly distributed, unpredictable random numbers. Because the indeterminate physical processes are completely determined by the dynamic parameters of the system, this type of entropy is categorized as dynamic entropy. Dynamic entropy sources that provide true randomness are usually extracted from the indeterminate physical processes, such as the jitter of ring oscillators (RO) and thermal noise from the digital-to-analog converter (DAC), or unpredictable events, such as the human-driven timing of mouse movements and keyboard strokes. There are two types of entropy source, dynamic and static entropy source, which can be generated from silicon chips. The higher the randomness of the keys, the higher the security of the data. It has been considered as a standard measurement of index to quantize the randomness of secret keys, which are used to protect the sensitive data. Since the concept of information entropy was introduced by Claude Shannon in 1948, entropy has been widely used in cryptography and cybersecurity.
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