@Tom see lol

Cryptography is a very exact (albeit heuristic, at the core, but nonetheless exceedingly exact) science. Asking if he has "seen the memory required for that" a bit insulting and telling. A level of detail far, far deeper than that is absolutely required for any useful analysis of a paper like that.

I really do like the idea, @Tom, so much that I've already analysed it and the fact of the matter is that a Bloom filter is necessarily a factor of $\frac{1}{\log(2)}$ larger than the raw list of hashes for the same security target. Any smaller bloom filter is trivially insecure due to a higher false positive rate.

@MickLH can you link to a paper that explains this result?

@Tom It's such a trivial result that I'm having trouble finding a paper dedicated to it. I can give you a whole stack of papers that all consider it "obvious"

compare to my previous message:

Basically it's one of the trivial details that nobody should have to explain, you can convince yourself using extremely basic algebra techniques to minimize the false positive rate.

It's well known in industry that bloom filters are a factor of $\frac{1}{\log(2)}$ away from optimal

I'm wondering, do you know the number of hash functions and bits used in the bloom filter will be chosen to meet the desired false probability rate for only a fraction of the 'public hashes' mentioned in the paper? for example: 1024 items per bloom filter, and so k=14 and m=19631

I do know, but even so it doesn't matter.

that's where I need the reference

That last sentence was nonsensical.

You're proposing something better than a Bloom filter, if it's competitive with straight Lamport signatures.

you say it doesn't matter, so now I would like to see proof

And you're proposing something that beats information theory, if it's competitive with practical signature schemes.

I have proven it.

Optimize the false positive rate.

Sit down and actually do it. Goodbye.

that's not a proof

@Tom That's not an expression giving the optimal false positive rate in terms of the data size.

The expression giving the optimal false positive rate in terms of the data size, is the proof.

So go do your homework, seriously. I'm done with you until you come back explaining what you learned.

If you make another procrastination I will put you on ignore.

you're so weird

you can use a bloom filter calculator to find any desired false positive probability

and it will give you k and m

sorry if you're messaging me, I ignored after the first message :(

and the libraries do this automatically

@EllaRose have you checked out the newest paper yet?

I looked at it. It did look friendlier with the psuedo code, though I will admit I have not seriously spent the time getting into whatever you're working on.

When it comes to those disposable signatures, to me it seems like the problem is barely about cryptography anymore is and is more about managing state/revocation

(which is a good thing, to have the hard part taken care of)

that's true, in practice managing state would be the bulk of a real life implementation

@EllaRose lol keeping a list of hashes is easy, the state management is the hard part :P

none of it would be especially hard though

I just think it's a novel method that can** be easily understood and can perform well, meaning it can be used in practice because many people can develop/use it, which isn't the same for the more complex schemes

@Tom I was hoping you'd investigated what I pointed out, I opened the channel so ping me if you get around to it eventually

No need to get personal, people.