Crack-Up (38 page)

“Most peculiar,” Dan muttered to himself more than once before looking up and saying, “Well, I can think of quite a number of potential approaches.”
 
Pitt abandoned his window.
 
“But since you’re offering payment for the striptease, I’d start by focusing on the population of female flight attendants in heavy financial debt.
 
If it’s a large one, we could, if we wish, include only those women who’d recently filed for bankruptcy, or developed the most severe patterns of delinquent payment.”

“By the way,” I said, “I want the striptease to take place on a corporate jet.
 
So our population would include only the corporation’s full-time staff, plus all the commercial flight attendants who’ve voluntarily put their names on a list to work the overflow.”

“Ah,” Dan said.
 
“Now the population is shrinking fast.
 
Quite fast indeed.”

“But the tricky part remains, doesn’t it?
 
Which of our financially strapped flight attendants would actually stoop to such a vulgar, embarrassing, some would say ‘sinful’ stunt?
 
For any amount of money?”

To his task Dan now warmed, beady eyes glimmering darkly.
 
“What I suggest we do is create what is known as a ‘psycho-graphic profile.’
 
In this case, a profile of the kind of woman who, in the face of heavy financial pressure, breaks the law and/or engages in anti-social behavior.
 
Commits bank fraud, or sells illicit drugs, or works in a strip club, etcetera.
 
We’d search through our data set for all the women in recent history who’ve actually done this kind of thing, following the onset of financial stress.

“Then we’d identify what they all share in common.
 
Data mining theory and practice informs us that we’d eventually find a unique cluster of characteristics and behaviors that predict with high precision which of the financially troubled flight attendants in our population pool would be the most willing to take her clothes off on the plane for money.”
 
Dan smiled for the first time, pleased with himself, or perhaps imagining his own naked flight attendant.

“Right,” I said, nodding to myself while recalling Pitt’s recent seminar.
 
“A cluster of qualities no human being would ever predict, or even entirely understand once the computer spits out its answer.
 
Some weird mix of factors.
 
Like parking tickets and pets and sibling order and lipstick color and a yen for rum raisin ice cream and Barbara Streisand CDs.”

“Something like that,” Dan said.
 
“The types of factors that end up being included in any predictive algorithm are often rather varied and non-intuitive, if not altogether surprising.”

I imagined the impersonator and his blank-faced cohorts building algorithms together, using this new science to narrow the search for a stripper down to two or three flight attendants or so, and then vetting the finalists further somehow—personally, perhaps—before settling on Elizabeth Hardtack.

“One more question, Dan.
 
Correct me if I’m wrong, but it seems to me that, given the unparalleled data access that you and the others in your shop enjoy—not to mention your access to the fabulous data crunching power of the agency’s super computers—that you could use your data mining skills to find anyone’s weakness, if you wanted to.
 
Anyone’s every weakness.”

“Well, sir,” Dan said, “the science isn’t perfect, nor the data, so mistakes are inevitable, but by and large, I should have to agree with you.”

“So much for the Fourth Amendment,” I said, unmasking my revulsion.
 
“So much for our constitutional protections against unreasonable searches and seizures.”

“Now, Argus—” Pitt said.

“You’re searching
souls
here, Pitt.
 
And without a warrant.”

“The dark part of the soul, yes.”

“What the hell gives you the right to learn more about people than they know about themselves?”

Pitt said to Dan, “Go to the bathroom, or something.”

But Dan couldn’t exit with me—my eyes not straying from Pitt’s—suddenly gripping him by the tie knot.
 
“God damn it, Pitt!
 
Can’t you see you’re out of line!
 
It’s for the public to decide how it is they want to balance their privacy rights versus their security needs!
 
Going behind their backs makes you and your partners in the rest of the intelligence community just a bunch of Peeping Toms with X-ray machines!”

“Enough!” Pitt said.

“Enough for now.”
 
I pulled Dan close—any closer and we’d be French kissing.
 
“Someone is data mining to commit crimes, Dan, not prevent them.
 
Sifting for the worst faults and weaknesses in people and then exploiting them.
 
Whoever it is has already arranged the murder of John Helms, and now threatens to have the president of these United States assassinated.”
 
Dan’s throat squeaked.
 
“What’s to say you’re not the bastard behind it all?”

“Oh, no, sir.
 
Oh, God, no.”
 
Squeak
.

“Now that I’ve told you this, you’ll have to be the first to be investigated, Dan, held indefinitely while Pitt has your office tossed, your home.”

“It’s not me, sir!
 
I swear!”
 
Squeak
.

“It’s got to be someone in your shop.
 
Or else in one of your sister shops in the FBI or
CIA
.”

“Don’t jump to conclusions,” Pitt told me.
 
“We could be dealing with an outsider.
 
A hacker, maybe.”

Over Pitt’s shoulder, I spotted my doppelganger again.
 
He was still skimming leaves with his long pool net.
 
Only now he was also sneaking anxious peeks at the darkness all around him.
 
My poor doppelganger!
 
He was paranoid too!

I released Dan.
 
“That true?
 
About hackers?”

Dan readjusted his tie.
 
“No, it’s impossible.”

“Why?” Pitt said.

“Our data warehouse isn’t online,” Dan said.
 
“The data we obtain from FDI—that’s Financial Datacorp International, the Wall Street consortium—gets taken down off the internet and put on a fully encrypted network that is only accessible from designated secure rooms in the Treasury building.”

“So it’s got to be an insider we’re after,” I said.

Dan shook his head.
 
“Not necessarily.
 
You see, FDI’s network is linked to the internet.
 
So are several local law enforcement networks.
 
Meaning they can be hacked.
 
So in theory, at least, all that data from those sites could be combined into one repository.
 
In which case, our nefarious data miner wouldn’t need any of the data we have—or the FBI has, or the
CIA
—to uh, wreak whatever havoc has been wreaked.”

“But who,” Pitt said, “would have that kind of skill?”

“Oh, thousands, sir.
 
Contrary to popular belief, it’s not difficult to break into even the most secure internet sites.
 
It’s the nature of our electronic infrastructure, which the computer industry designed without much thought for security.
 
It’s also the increasing complexity of software code.
 
The programmers now write millions of lines of code for a single program, making flaws and misconfigurations inevitable.”

I’d learned just enough from computer security experts over the years to add, “There isn’t a corporate or government network that isn’t broken into half a dozen times a year or more.”

“That’s right,” Dan said.
 
“And the break-in rates keep going up and up.
 
Even the script kiddies can do it now.”

“ ‘Script kiddies?’ ” Pitt said.

“Unskilled hackers,” I said, “who run automated programs they download off the internet to poke and prod at computer networks all over the world, searching for vulnerabilities.
 
Skilled hackers look down on them.”

My doppelganger had dropped his pool net and rushed to the window.
 
He pounded away now on the glass.
 
I retreated a step.

“What?” Pitt said to me.
 
“What is it?”

I pointed at my doppelganger, his (my) face fear-contorted, darting left and right with bird-quick glances, as if the nearby darkness cloaked some unworldly monster army ready to spring.
 
All the while, his hands (my hands) kept pounding on the glass.
 
He froze suddenly, staring me in the eyes (with my eyes) and let out a terrified scream.
 
I screamed back.

“I don’t see anything,” Pitt said, and I myself didn’t see—or hear—Pitt after that, nor Dan the data miner.

My doppelganger’s open-palmed pounding on the window grew louder and harder and the glass cracked and I thought it might at any moment shatter.
 
He called to me—in my own voice.
 
“Run away!
 
Run away!
 
Run away!”

I ran away.
 
I bolted from Pitt’s house through the front door, scaled the fence in one, adrenalin-fueled hop, and sprinted off down the street at full tilt, my miserable life hanging in the balance, I had no doubt.

Chapter 40

I know now that I spent the next five days wandering the streets of
Washington
,
DC
psychotic.
 
I ate from trash cans, when I wasn’t feeling proud, and from dumpsters in back alleys, when I was.
 
I slept in city parks—or not at all.
 
I ran from stampeding spiders, a thousand or more at once, and from a single, hollow-eyed zombie child who smelled of rotting flesh.
 
I chased, but never could quite catch, a mischievous, blue-haired dwarf, who would lather green slime on park benches and hand railings and other places that I touched.
 
I lined my underwear with discarded aluminum foil to prevent spy satellites from tracking me.

And yet I found myself being tracked, followed by men dressed in dark suits and sunglasses, like Secret Service agents.
 
I believed them to be—felt I knew them to be—members of the impersonator’s secret cabal.
 
They would never get too close to me, so sometimes I would shout at them:
 
“I know how you did it!”

What I knew—and what I whispered into my tape recorder—was how they’d conspired to murder John Helms: with that new science called data mining, with its intrusive revelations and uncanny predictions about human beings.
 
It was how I’d originally been identified as a paranoid schizophrenic, I was fairly sure.
 
And I was fairly sure the cabal knew everything there was to know about me—more even than I knew about myself.

But who are they
? I’d wonder—whenever I managed to focus on the problem, for my disease-shortened attention span shrank day by day.
 
Meanwhile, new distractions abounded.

One night, while I was sleeping in
Rock
Creek
Park
, someone stole the shoes and socks off my feet.
 
Another homeless creature had stolen them, no doubt, but at the time I couldn’t decide whether to blame forces from another dimension or the blue-haired dwarf.
 
The pavement proved quite hot on my feet during the day.