How good are you at predicting the outcome of rolling a die? ‘Probably not very good, and even with a 30 cubical die, any outcome has a 1-in-30 chance. While it’s frustrating to predict the outcome of simple dice rolls, we know that we can see a number repeat if we roll the dice just a few more times. A cubical die is a random number generator. It’s a physical random number generator that will give us a number between 1 and 30. Other simple random number generators in our everyday lives are drawing from a deck of cards and coin flipping (a really simple one for sure).

Still, with all these and other similar methods, we have two special situations. The first is that it’s not entirely unreasonable to guess the outcome beforehand. This is handy if you’re betting on the outcome, and the odds are higher the more possible outcomes there are (one in two for the coin toss, one in six for the die, one in fifty two for the cards). The second is that the outcomes repeat fairly regularly. Again, they repeat less frequently on average, as the number of outcomes increases.

So what if we want to generate random numbers that are next to impossible to guess and that never repeat? Why would we want to do something like that?

The security paradigm in modern cryptology has been shifted from ciphers to keys since many ciphers are broken as a result of progressive developments in computer science. In modern cryptology, secrecy is based on keys which are basically random numbers (Fairy dust for unpredictability). A cryptosystem is only as secure as its random number generator.

A failure in the random number generation mechanism can easily become a failure for the overall crypto system. (Achilles Heel Metaphor)

An ideal random number generator  is a computational or a physical device which generates a stream of Statistically independent, Identically distributed,Unpredictable,numbers or symbols that create a discrete memoryless information source with positive entropy.