“Wait,” Mom said. “How do you know how my son uses the Muni?”
“The Fast Pass,” he said. “It tracks voyages.”
“I see,” Mom said, folding her arms. Folding her arms was a bad sign. It was bad enough she hadn’t offered them a cup of tea — in Mom-land, that was practically like making them shout through the mail-slot — but once she folded her arms, it was not going to end well for them. At that moment, I wanted to go and buy her a big bunch of flowers.
“Marcus here declined to tell us why his movements had been what they were.”
“Are you saying you think my son is a terrorist because of how he rides the bus?”
“Terrorists aren’t the only bad guys we catch this way,” Zit said. “Drug dealers. Gang kids. Even shoplifters smart enough to hit a different neighborhood with every run.”
“You think my son is a drug dealer?”
“We’re not saying that —” Zit began. Mom clapped her hands at him to shut him up.
“Marcus, please pass me your backpack.”
I did.
Mom unzipped it and looked through it, turning her back to us first.
“Officers, I can now affirm that there are no narcotics, explosives, or shoplifted gewgaws in my son’s bag. I think we’re done here. I would like your badge numbers before you go, please.”
Booger sneered at her. “Lady, the ACLU is suing three hundred cops on the SFPD, you’re going to have to get in line.”
Mom made me a cup of tea and then chewed me out for eating dinner when I knew that she’d been making falafel. Dad came home while we were still at the table and Mom and I took turns telling him the story. He shook his head.
“Lillian, they were just doing their jobs.” He was still wearing the blue blazer and khakis he wore on the days that he was consulting in Silicon Valley. “The world isn’t the same place it was last week.”
Mom set down her teacup. “Drew, you’re being ridiculous. Your son is not a terrorist. His use of the public transit system is not cause for a police investigation.”
Dad took off his blazer. “We do this all the time at my work. It’s how computers can be used to find all kinds of errors, anomalies and outcomes. 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. It’s part of something called Bayesian analysis and it’s been around for centuries now. Without it, we couldn’t do spam-filtering —”
“So you’re saying that you think the police should suck as hard as my spam filter?” I said.
Dad never got angry at me for arguing with him, but tonight I could see the strain was running high in him. Still, I couldn’t resist. My own father, taking the police’s side!
“I’m saying that it’s perfectly reasonable for the police to conduct their investigations by starting with data-mining, and then following it up with leg-work where a human being actually intervenes to see why the abnormality exists. I don’t think that a computer should be telling the police whom to arrest, just helping them sort through the haystack to find a needle.”
“But by taking in all that data from the transit system, they’re creating the haystack,” I said. “That’s a gigantic mountain of data and there’s almost nothing worth looking at there, from the police’s point of view. It’s a total waste.”
“I understand that you don’t like that this system caused you some inconvenience, Marcus. But you of all people should appreciate the gravity of the situation. There was no harm done, was there? They even gave you a ride home.”
They threatened to send me to jail, I thought, but I could see there was no point in saying it.
“Besides, you still haven’t told us where the blazing hells you’ve been to create such an unusual traffic pattern.”
That brought me up short.
“I thought you relied on my judgment, that you didn’t want to spy on me.” He’d said this often enough. “Do you really want me to account for every trip I’ve ever taken?”
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.
It’s got lots of applications — you can ask a computer to count the lines in a picture and see if it’s more like a “dog” line-frequency histogram or a “cat” line-frequency histogram. It can find porn, bank fraud, and flamewars. Useful stuff.
And it was bad news for the Xnet. Say you had the whole Internet wiretapped — which, of course, the DHS has. You can’t tell who’s passing Xnet packets by looking at the contents of those packets, thanks to crypto.
What you can do is find out who is sending way, way more encrypted traffic out than everyone else. For a normal Internet surfer, a session online is probably about 95 percent cleartext, five percent ciphertext. If someone is sending out 95 percent ciphertext, maybe you could dispatch the computer-savvy equivalents of Booger and Zit to ask them if they’re terrorist drug-dealer Xnet users.