Mixmaster Message Drops

[Jim Choate]

On Thu, 9 Aug 2001, Nomen Nescio wrote:

> My studies show about 1 chance in 20 that any particular running
> remailer will drop a message.  This is based on sending a bunch of
> messages through chains of remailers of varying length and seeing how
> many arrive in a day or so, and then calculating the average
> probability.  These are not the lame remailers, either, these are the
> cream of the crop, based on various performance reports.
> 
> Note that in a chain of twenty remailers this causes many messages to
> be dropped.  Even if we achieve a reliability rate of 1 in 100,000,
> this means about 1 in 5000 messages will be lost through a chain of 20
> remailers - usable.
> 
> For some reason remailer culture has accepted message drops as normal.
> They aren't normal.  There isn't any reason why a remailer should be
> any less reliable than a mail server.  I estimate mail servers are far
> more reliable than 1 loss in 100,000 messages.

Consider how few messages get lost in the CDR. The extra mix technology
shouldn't inject any randomness in message processing. You should drop
messages measured in the handful per year max.

The next major question is to determine where the drops are happening.
Inbound, outbound, inter-remailer, intra-remailer?

One aspect of this, assuming the remailers are under attack and that is
the hypothesis we are going to assume, is that we need to be able to
inject traffic into the remailer stream anonymously. Otherwise Mallet
get's wise to what is going on and starts playing us.

If at all possible all measurements should be made anonymously and as
stealthily as possible.

Q: How to inject traffic into the remailer network anonymously?

Q: How do we measure the input/output flow without collusion of the
   operator?

Q: Where are the computing resources to munge resulting flood of data over
   at least a few weeks time period. How do we hide this 'extra' flow of
   data? It represents an opportunity for incidental monitoring due to
   load usage.

Q: How do we munge the data? What are we trying to 'fit'?

Q: Once we have the data and can (dis)prove the hypothesis, then what?


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