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One of the main problems with PRNGs is that we cannot control the degrees of freedom available to the random number generator. The computer has no direct input in generating the sequence. The deterministic nature of a PRNG creates much less randomness than a true random generator. The main characteristic of the PRNG is that there is a priori assurance that the output is always the same because of the use of the seeds (possibilities to calculate the previous value of the sequence). This leads to many potential problems. RNGs should follow the entropy guideline proposed by NIST to be “strong” (i.e., generating truly random sequences). The NIST recommended guidelines call for a minimum size of 128 bits. If the entropy value is much lower than 128 bits, the probability of a sequence re-calibration is high. The exact entropy value of a RNG is based on the quality of the PRNG, including its state size and any security flaws.
The data is quite simple, more or less. A secret code is mapped to a password. The reverse mapping is used to verify the password. All I need is a hash function and a true random number generator. I use an 8-bit secret code where the password is a reverse mapping of the secret code. Each character is mapped to a hash of the code by the hash function. True random number generators in this context are hard to use because there are few ways of ensuring entropy is preserved as numbers are being produced. I am free to choose what hash function to use, so long as it is not compromised. I use SHA-256 since it is readily available and is a fairly solid hash function. SHA-256 takes an input and produces a 256-bit hash. Here is the code for my SHA-256 implementation. It is a 128-bit hash function so there is some additional overhead from converting a 32-bit integer to a 128-bit integer. (This overhead is in addition to the SHA-256 output).