I hooked up my Xbox as soon as I got to my room. I'd bolted the projector to the ceiling so that it could shine on the wall over my bed (I'd had to take down my awesome mural of punk rock handbills I'd taken down off telephone poles and glued to big sheets of white paper).

I powered up the Xbox and watched as it came onto the screen. I was going to email Van and Jolu to tell them about the hassles with the cops, but as I put my fingers to the keyboard, I stopped again.

A feeling crept over me, one not unlike the feeling I'd had when I realized that they'd turned poor old Salmagundi into a traitor. This time, it was the feeling that my beloved Xnet might be broadcasting the location of every one of its users to the DHS.

It was what Dad had said: You ask the computer to create a profile of an average record in a database and then ask it to find out which records in the database are furthest away from average.

The Xnet was secure because its users weren't directly connected to the Internet. They hopped from Xbox to Xbox until they found one that was connected to the Internet, then they injected their material as undecipherable, encrypted data. No one could tell which of the Internet's packets were Xnet and which ones were just plain old banking and e-commerce and other encrypted communication. You couldn't find out who was tying the Xnet, let alone who was using the Xnet.

But what about Dad's "Bayesian statistics?" I'd played with Bayesian math before. Darryl and I once tried to write our own better spam filter and when you filter spam, you need Bayesian math. Thomas Bayes was an 18th century British mathematician that no one cared about until a couple hundred years after he died, when computer scientists realized that his technique for statistically analyzing mountains of data would be super-useful for the modern world's info-Himalayas.

Here's some of how Bayesian stats work. Say you've got a bunch of spam. You take every word that's in the spam and count how many times it appears. This is called a "word frequency histogram" and it tells you what the probability is that any bag of words is likely to be spam. Now, take a ton of email that's not spam -- in the biz, they call that "ham" -- and do the same.

Wait until a new email arrives and count the words that appear in it. Then use the word-frequency histogram in the candidate message to calculate the probability that it belongs in the "spam" pile or the "ham" pile. If it turns out to be spam, you adjust the "spam" histogram accordingly. There are lots of ways to refine the technique -- looking at words in pairs, throwing away old data -- but this is how it works at core. It's one of those great, simple ideas that seems obvious after you hear about it.