Dr. J. Lindqvist's work on the front page of NSF web site

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Rutgers researchers have shown that GPS technology is not needed to show where a driver traveled. A starting point and the driver's speed are enough.

In our constantly connected, information-rich society, some drivers are jumping at the

chance to let auto insurance companies monitor their driving habits in return for a handsome discount on their premiums. What these drivers may not know is that they could be revealing where they are driving, a privacy boundary that many would not consent to cross.

A team of Rutgers University computer engineers has shown that even without a GPS device or other location-sensing technology, a driver could reveal where he or she traveled with no more information than a starting location and a steady stream of data that shows how fast the person was driving.

Insurance companies and customers both have incentive to monitor driving speeds, said   Janne Lindqvist, assistant professor in the Department of Electrical and Computer Engineering at Rutgers. Drivers who avoid jackrabbit starts and sudden stops are typically lower-risk drivers, and insurance companies benefit by rewarding such behavior. So some companies are offering lower premiums to customers who install a device that constantly measures, records and reports their speed.

“The companies claim this doesn’t compromise privacy, because all they are collecting is your speed, not your location,” said Lindqvist, who is also a member of the university’s Wireless Information Network Laboratory, or WINLAB. “But we’ve shown that speed data and a starting point are all we need to roughly identify where you have driven.”

Reproducing an exact driving path from this limited and basic information is challenging – and it is less precise than using GPS or cellular signal tracking measurements. But with the researchers’ approach, sometimes even one drive is enough to reveal the person’s destination within a third of a mile or less.

The technique, dubbed “elastic pathing,” predicts pathways by seeing how speed patterns match street layouts. Take for example, a person whose home is at the end of a cul-de-sac a quarter mile from an intersection. The driver’s speed data would show a minute of driving at up to 30 miles per hour to reach that intersection. Then if a left turn leads the driver to a boulevard or expressway but a right turn leads to a narrow road with frequent traffic lights or stop signs, you could deduce which way the driver turned if the next batch of speed data showed a long stretch of fast driving or a slow stretch of stop-and-go driving. By repeatedly matching speed patterns with the most likely road patterns, the route and destination can be approximated.

For more information please see   http://nsf.gov