How do you measure the amount of “true randomness”? CSPRNGs can use very little entropy to generate large amounts of random data. Mathematically speaking there isn’t any difference between that and what you call “true randomness” - if there was, they wouldn’t be CSPRNGs.
KairuByte@lemmy.dbzer0.com 11 months ago
Truly random would be something that is impossible to reproduce. While you are correct that we can approximate randomness, the final calculation can always be replicated if the initial inputs are known. Just because something is exceedingly difficult to replicate, doesn’t mean it is truly random.
Think of it like cleaning your pool. You have a vacuum, chemicals, the system circulates, maybe a skimmer or a net. You can get the pool to the point that it is acceptable to swim in, but you’re never actually swimming in a clean pool. In a similar manner, current random number generators get you to a point that you are (usually) fine assuming the number is random, but it never really is.
FooBarrington@lemmy.world 11 months ago
I know what you’re trying to get at, but my point is this: Imagine you have two streams of data, one from a CSPRNG, and one from what you call “true randomness”. How can you tell which one is which (as long as you’re staying under the CSPRNGs limit from your initial entropy)?
If you can’t tell me a way, there is no functional difference between these two options. So what advantage would true randomness hold?
KairuByte@lemmy.dbzer0.com 11 months ago
I said this in another comment, but while I agree that there is virtually no functional difference, and in the vast majority of cases truly random and functionally random are equivalent, that doesn’t mean that something which is functionally random is truly random.
FooBarrington@lemmy.world 11 months ago
But it is truly random for all intents and purposes, since the input is truly random. Just because the process contains deterministic steps doesn’t mean the input entropy isn’t true entropy anymore.