Exploring Probabilistic Prefix Search in Crypto

This method revolves around calculating the chances of a specific prefix showing up in a set of hashes, while ignoring the less likely outcomes.

So how do you determine if that partial match actually indicates the target is in that range?

I honestly can't grasp what this code is trying to do or how to utilize it.

Can you show a demo or example?

You can't really be sure about anything with probabilistic software. Just because some ranges are less likely doesn’t mean you’ll definitely succeed.

So you’re basically hunting for the hex prefix of the address?

No way, it's super unlikely to find 5 "abcde" prefixes in a 65536 range.

Actually, it’s 93.94% likely that you won’t find any "abcde" prefix in that range. So 6.06% is the chance of finding it at least once.

What about the claim of 3% you mentioned earlier?

Yes, I’m focused on h160 prefixes.

You’re misreading the stats, dude. 3% of 65536 is like 2000. The collisions are way more than just a few. Did you even test the script?

I’ve been working on a similar thing for another puzzle... Sometimes had 2000 keys between two "145d" points and at other times, 120k. It varies a lot.

Haven't tried with public keys yet. They’ve got similarities, but they’re not identical to hashes. Testing is key here.

Let’s reset things. So you got to 2000 collisions based on your 65537 keys. Is that where the 3% number is from?

That’s not how uniform distribution works. Check your math.

You need to rethink the theoretical approach. Prefixes work differently depending on the set of hashes used.

So... what’s the conclusion? Your theory seems solid but show me the proof.

My script does track how often prefixes show up. It’s all about the frequency of those appearances compared to what’s expected.

You're estimating based on your setup. The longer the prefix, the lesser chance of collisions. Are we on the same page?

That’s not true! My script calculates the frequency of each prefix consistently.