Technology

A Privacy-Friendly Way to Ban Gambling Addicts From Casinos


Ann Cavoukian, Ontario Information and Privacy Commissioner

Photograph by Ontario Information and Privacy Commission

Ann Cavoukian, Ontario Information and Privacy Commissioner

The Canadian province of Ontario has 27 government-run casinos. As you might expect, thousands of people flock to these gaming establishments every year. And 15,000 flock to them too much. They’re self-confessed gambling addicts.

In Ontario, gambling addicts can ask to be put on a list that bans them from entering a casino. They volunteer a photo, which is then distributed around the casinos. When a worker spots one of the addicts in a casino, the addict is meant to welcome being booted from the premises.

Traditionally, this process has not worked too well. For one, casino staff struggled to commit the faces of hundreds or thousands of gamblers to memory. And then, when a worker did slip up and let a banned gambler into a casino, the gambler sometimes filed a lawsuit against the government for being negligent in its banning duties.

This may sound nuts, but it happens to be true, says Ann Cavoukian, Ontario’s Information and Privacy Commissioner. “They would sneak in and gamble away their lives and lose their families and jobs and homes, and then sue the government, saying, ‘You said you were going to keep me out,’” Cavoukian says. “The state was losing millions to this.”

Three years ago, Ontario set out to rectify the situation by using facial recognition cameras. It placed the cameras near casino doors and tuned them to spot the 15,000 people on the no-gambling list. An addict entering a casino triggered an alarm. “Once there’s a match, a security guard manually does a secondary check and then approaches the person and says, ‘It is our understanding you would like us to escort you out. Is that still your wish?’” she said.

As it happens, Cavoukian is something of a big name in the privacy and biometric worlds, so to see this type of facial recognition technology being used could come as a surprise. Cavoukian admits that it did not pass “the yuck factor” at first, particularly the thought of thousands of non-addicts having their faces sucked into a database.

And so the Ontario system was designed to support some cutting-edge privacy measures. First, it does scan the face of each person entering a casino, but if there is no match against the list of 15,000 addicts, the image is removed instead of being stored in a database. Second, the casinos use a form of biometric encryption for the face and personal information databases. Essentially, this means that the personal information is stored in an encrypted fashion and can be unlocked only when a face serves as a key. If a hacker were to break into the database, he would find only garbled strings of numbers and letters.

Cavoukian has become a major proponent of biometric encryption as technology such as facial recognition and iris scanning gains hold. “I just don’t want any identifiable biomarkers out there,” she says.

Cavoukian also fears that people have become too free with their faces. “Facebook (FB), man, that is a disaster waiting to happen,” Cavoukian says. “You won’t have to go ask the FBI to use the 12 million faces in their database. You can just go to Facebook. People don’t know what they’re giving away.”

As for the casinos in Ontario? Well, the government has yet to face a lawsuit since the facial recognition scanners were activated.

Vance_190
Vance is a technology writer for Bloomberg Businessweek in Palo Alto, Calif. He is the author of Elon Musk: Tesla, SpaceX, and the Quest for a Fantastic Future (HarperCollins, May 2015). Follow him on Twitter @valleyhack.

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