> The problem you'd have to solve here is basically the subset sum problem: given a set of transfers in an out of a mixer (let's say you already know which addresses it uses, which is not easy since it can make new ones for free) which subsets of the transfers out have the same totals as which subsets of transfers coming in?
This isn't even the right problem, given the usage model that I posited and to which mootothemax replied; if you keep a balance stored in the mixing account, to which you make deposits on a regular payments, then it's highly unlikely that outgoing payments will match incoming payments in the first place. How often do you currently deposit checks into your bank account in exactly the same amount as outgoing payments that you immediately write after making the deposit?
There'd be no conclusively correlating information here; payments into the mixing account would have different amounts and timestamps from payments going out of it, and in order to use inferences taken from patterns as identifying information - e.g. someone orders a pizza from Mario's Pizzeria every Tuesday at 7 PM - you'd have to already have identifying information about the person you're trying to find in the first place, e.g. that I live near Mario's and happen to enjoy their pizza.
It's not just that the complexity of the problem increases with scale, it's that the reliability of the correlations you can make also decreases with scale.
This isn't even the right problem, given the usage model that I posited and to which mootothemax replied; if you keep a balance stored in the mixing account, to which you make deposits on a regular payments, then it's highly unlikely that outgoing payments will match incoming payments in the first place. How often do you currently deposit checks into your bank account in exactly the same amount as outgoing payments that you immediately write after making the deposit?
There'd be no conclusively correlating information here; payments into the mixing account would have different amounts and timestamps from payments going out of it, and in order to use inferences taken from patterns as identifying information - e.g. someone orders a pizza from Mario's Pizzeria every Tuesday at 7 PM - you'd have to already have identifying information about the person you're trying to find in the first place, e.g. that I live near Mario's and happen to enjoy their pizza.
It's not just that the complexity of the problem increases with scale, it's that the reliability of the correlations you can make also decreases with scale.