Crack-Up (36 page)
Authors: Eric Christopherson
“How did you find out about—”
“I’m in a hurry, Pitt.”
He sighed.
“They say never argue with a paranoid.”
“That’s right.”
“John Helms was a patriot,” Pitt said.
He began to pace in front of his office window overlooking a rectangular pool, lit by underwater flood lights as well as bronze oil lamps dangling from spear-like poles at the corners.
His hair had turned from gray to white in the time I’d known him, but it was still thick, and still combed in a preacher-style pompadour.
I sat down in a Chippendale armchair.
“Some would say John was more than a patriot.
A jingoist.
What’s your point?”
“He was doing us a favor.
On a project of his.”
“Who’s us?”
“FBI,
A soft, steady hum pervaded Pitt’s 18th century-style home office.
It was from a white noise generator, busy drowning out any bugs and wiretaps.
The windows, he’d once told me, had been tinted to bar snooping from infrared spy satellites.
The worst dangers lurked below the surface of things.
I heard
their
hum everywhere I’d go now.
“And DARPA?” I said.
“They were lending one kind of expertise, John another.”
“What project are we talking about?
What kind of favor?”
“The best, Argus.
Beyond what you could imagine.”
Pitt sat down in the matching armchair facing me.
“You see, we in the
US
intelligence community have ourselves a new trade secret.
One that changes everything.”
Chapter 38
Now it wasn’t merely the wavy pompadour lending Nathan Pitt the look of a preacher.
Now the Secret Service director’s deep-set, smoke-gray eyes smoldered with fervor.
He inched his Chippendale armchair closer to me before leaning forward.
“Do you recall the brouhaha over DARPA’s Total
It was a few years ago now.
Tons of press.
All the privacy rights groups crying, ‘Big Brother.’ ”
“Vaguely, I recall.
Weren’t the Democrats in Congress trying to kill the project?”
“They succeeded.
What do you know about the project itself?”
“Not much.
Some sort of massive domestic surveillance system.
To fight terrorism.”
“That’s the official story.
For political reasons, DARPA never mentioned the system’s potential to address other national security issues besides terrorism, as well as domestic crime.”
“But the ACLU,” I said, “and the civil libertarians saw the potential anyway, right?”
“They saw what they always see, the potential for abuse.
In this case, abusive government spying on private citizens.
But for once they couldn’t do us any harm, as it turned out.”
“No?
I thought you said Congress quashed the project?”
“It did, it did.
But what Congress failed to halt was the private sector’s own, parallel project.
Its own massive domestic surveillance system.”
“What are you talking about?”
Pitt smiled.
“It all started years ago, when the private sector began collecting immense amounts of raw data on American citizens.
But they couldn’t do much with it until recently, until the costs of computer disk storage and computer processing power had dropped dramatically.”
“What kind of data have they been collecting?”
“Oh, you name it!”
Pitt threw up his hands and leaned way back in his Chippendale as if about to be swamped by a tidal wave of record-keepings.
“It’s all for sale these days.
Personal data from off your driver’s license or marriage certificate.
Financial records of all kinds—everything from your ATM withdrawals to your credit card purchases, to your brokerage account balance—and all your insurance policies, and pension and disability records, and your utilities records too—your monthly telephone, electricity, gas, and cable TV charges.
Your real estate info, and what you buy from the grocery store, if you use a club card.
What groups or organizations you belong to, based on membership rosters.
“What videos you rent at Blockbuster.
Your own academic background, your personal employment history, any court records, any criminal history.
The number of traffic fines and parking tickets.
If you go online—and half the country does now—then where you go and what you do gets recorded in log files or via ad company ‘cookies,’ because every click of the mouse is a digital gesture, revealing your habits, preferences, and tendencies.
I could go on and on.
But in sum, the daily compiling and transfer of consumer information is staggering in quantity.
There are companies devoted entirely to its collection.
One company claims to have data on 98 percent of all households in
America
.”
“So what do they do with it all?”
“They mine it.
For gold.
They call it, ‘data mining,’ in fact.
They feed their mountains of data to their super computers, and the computers spit out the gold.
It boggles the imagination, Argus, the computer power we possess today.
Take, for example,
Did you know that it would take one person with a hand-held calculator seventeen million years of labor, working day and night, to complete the calculations Blue Streak can do in one second?
One second!”
“Seventeen million years, working day and night?
I’ve had jobs like that.”
Pitt smiled.
“Haven’t we all?”
He slapped me on my bad shoulder.
It hurt.
“Tell me about the gold.”
“The gold in the
Pitt left his chair to pace his Persian carpet.
“Data mining digs up hidden knowledge—subtle relationships and patterns previously undetected, buried, if you will, within these immense, staggering mountains of information.
Knowledge of complex, multifactor dependencies.
Knowledge the feeble human brain could never find on its own.
And knowledge sufficient to make uncannily accurate predictions about what individual consumers will purchase, or would purchase, under various hypothetical scenarios.”
I sat up straighter.
A cold wisp of fear fluttered in my belly now, my paranoia-powered early warning system activated.
Pitt noticed the change.
“What is it?”
“I’m not sure yet.
Go on.”
“Right.
Well, these hidden patterns and relationships are often quite surprising, even counter-intuitive.
For example, one of the earliest commercial adopters of data mining was a company called, Skymall.
You find their mail-order catalog stuffed inside the pockets of airplane seats on several major carriers.
If you wish to buy an item, you visit their website or phone their call center.
Either way, once the initial purchase is made, the credit card number is used as a unique identifier to access all available information on the purchaser from the data warehouse.
Then their data mining software—often known as ‘predictive analytics,’ by the way—crunches that data and spits out a customized recommendation for the purchaser to make another, follow-up purchase.
“The first time Skymall used its new software, they thought it was haywire.
A gentleman phoned to order a twenty-eight dollar cloth shirt.
The predictive analytics told the clerk taking the call to suggest to this gentleman that he also consider purchasing a very expensive cigar humidor—even though there was no data hinting that the man even smoked.
None at all.
And guess what?”
“He bought the humidor?” I said.
“He did indeed.”
Pitt halted by his antique writing table, veneered in rosewood, with brass ornaments.
He perched his fanny on a front corner.
“Skymall had less than a dozen data points on the buyer.
His age.
His zip code.
His marital status.
His home-owner status.
His previous purchase of a certain set of blue bathroom towels, and a handful of other seemingly benign bits of information, none having anything remotely to do with cigars.”
“There was no guarantee the man would buy the humidor—”
“Of course not, Argus, of course not.
Yet data mining had revealed that he was
likely
to, because he shared a previously unrecognized cluster of characteristics with those customers who had bought humidors from Skymall in the past.
Do you know what a ‘doppelganger’ is, Argus?”
“Sure.
It’s an old superstition that says, for every one of us alive today, there’s a duplicate somewhere, living in another part of the world.
A non-relative, but a twin just the same.”
“It’s not a superstition, Argus, not really.
None of us are nearly as unique as we’d like to believe.
We value freedom and individuality in large measure because in truth we’re not so free, and not so individual either.
We’re all predictable, Argus, and we all have doppelgangers.
Not one, but many—an army of them—now that planet Earth has grown to six billion people.”
“If that’s true, I’m not sure I’d like to meet any of mine.”
Pitt laughed.
“Nor I, nor I.”
He resumed his rug pacing.
“Anyway, for obvious reasons, big business is jumping on this new technology in a big way.
The banks, for example, are using it to detect money laundering and to screen credit card transactions for fraud.
The insurance companies use it to screen out individuals who pose bad risks.
Drug companies use it to devise marketing campaigns that will encourage physicians to prescribe more drugs.
There’s a major retailer now using it to develop ‘psycho-graphic profiles’ of its customers, to identify . . .
“You don’t like the sound of this, Argus, that’s plain to see.
But I understand.
It is kind of eerie, as a matter of fact, and the eeriest thing to me, personally, is that, every time the data mining machine discovers some new pattern or relationship in the data, we humans more often than not still fail to understand it.
Why would a man’s preference in bath towels be linked to his desire for a cigar humidor?
It’s a mystery.
But luckily one we know how to exploit.”
