“With very limited amounts of driving data we can enable very powerful and accurate inferences about the driver’s identity,” says Miro Enev, a former University of Washington researcher who worked on the study before taking a job as a machine-learning engineer at Belkin. And the researchers argue that ability to pinpoint could have unexpected privacy implications: Everything from letting insurance companies punish drivers who loan their cars to their teenage kids, to confirming the identity of a driver who violated traffic laws or caused a collision.
In the end, the researchers found that they didn’t even need the longest portion of the driving test to reliably identify each of the 15 drivers. Using the full collection of the car’s sensors—including how the driver braked, accelerated and angled the steering wheel—the researchers found that their algorithm could distinguish each of the drivers, with 100 percent accuracy, based on only 15 minutes of the driving data. Even with data from the brake pedal alone, they found that they could guess at the correct driver with 87 percent accuracy.
Keep in mind this is result on identify verification. They are not determining identity with this information. Still, this very interesting. Not all the implications are obvious at first glance. But it might be claimed that regular collection of this data violates my Jews In the Attic Test.