Video - Pseudorandom Number Generators
Random vs. Pseudorandom Number Generators. Created by Brit Cruise.
When we observe the physical world we find random fluctuations everywhere. We can generate truly random numbers by measuring random fluctuations, known as noise. When we measure this noise, known as sampling, we can obtain numbers.
One, two, three, four– For example, if we measure the electric current of TV static over time, we will generate a truly random sequence. We can visualize this random sequence by drawing a path that changes direction according to each number, known as a random walk. Notice the lack of pattern at all scales.
At each point in the sequence the next move is always unpredictable. Random processes are said to be nondeterministic, since they are impossible to determine in advance. Machines, on the other hand, are deterministic. Their operation is predictable and repeatable.
In 1946, John von Neumann was involved in running computations for the military; specifically involved in the design of the hydrogen bomb. Using a computer called the ENIAC, he planned to repeatedly calculate approximations of the processes involved in nuclear fission.
However, this required quick access to randomly generated numbers that could be repeated, if needed. However, the ENIAC had very limited internal memory; storing long random sequences was not possible. So, Neumann developed an algorithm to mechanically simulate the scrambling aspect of randomness as follows: First, select a truly random number, called the “seed”.
This number could come from the measurement of noise, or the current time in milliseconds. Next, this seed is provided as input to a simple calculation. Multiply the seed by itself, and then output the middle of this result. Then you use this output as the next seed, and repeat the process as many times as needed.
This is known as the middle-squares method and is just the first in a long line of pseudorandom number generators. The randomness of the sequence is dependent on the randomness of the initial seed only. Same seed, same sequence. So, what are the differences between a randomly generated versus pseudorandomly generated sequence? Let’s represent each sequence as a random walk.
They seem similar until we speed things up. The pseudorandom sequence must eventually repeat. This occurs when the algorithm reaches a seed it has previously used, and the cycle repeats. The length, before a pseudorandom sequence repeats, is called “the period”.
The period is strictly limited by the length of the initial seed. For example, if we use a two-digit seed, then an algorithm can produce, at most, 100 numbers, before reusing a seed and repeating the cycle. A three-digit seed can’t expand past 1,000 numbers before repeating its cycle, and a four-digit seed can’t expand past 10,000 numbers before repeating.
Though if we use a seed large enough, the sequence can expand into trillions and trillions of digits before repeating. Though the key difference is important. When you generate numbers pseudorandomly, there are many sequences which cannot occur.
For example, if Alice generates a truly random sequence of 20 shifts, it’s equivalent to a uniform selection from the stack of all possible sequences of shifts. This stack contains 26 to the power of 20 pages, which is astronomical in size. If we stood at the bottom and shined a light upwards, a person at the top would not see the light for around 200,000,000 years.
Compare this to Alice generating a 20 digit pseudorandom sequence, using a four-digit random seed. Now, this is equivalent to a uniform selection from 10,000 possible initial seeds, meaning she can only generate 10,000 different sequences, which is a vanishingly small fraction of all possible sequences.
When we move from random to pseudorandom shifts, we shrink the key space into a much, much smaller seed-space. So, for a pseudorandom sequence to be indistinguishable from a randomly generated sequence, it must be impractical for a computer to try all seeds and look for a match.
This leads to an important distinction in computer science, between what is possible, versus what is possible in a reasonable amount of time. We use the same logic when we buy a bike lock. We know that anybody can simply try all possible combinations, until they find a match and it opens.
But it would take them days to do so. So, for eight hours we assume it’s practically safe. With pseudorandom generators, the security increases as the length of the seed increases. If the most powerful computer would take hundreds of years to run through all seeds, then we safely can assume it’s practically secure, instead of perfectly secure.
As computers get faster the seed size must increase accordingly. Pseudorandomness frees Alice and Bob from having to share their entire random shift sequence in advance. Instead, they share a relatively short random seed, and expand it into the same random-looking sequence when needed. But what happens if they can never meet to share this random seed?