The team then used information gleaned from Facebook profiles to guess the birth dates or birthplaces of the people that the software had accurately identified. With that information, they of each person's Social Security number and were accurate about 27 percent of the time.

"The bigger picture here was to show that we're getting closer to a world where online and offline data blend seamlessly, where you can start with an anonymous face in the street and you can end up identifying something extremely sensitive about the person by combining these different technologies," says the leader of the team, Carnegie Mellon assistant professor Alessandro Acquisti.

While the demonstration by Acquisti's crew may make anyone who cares about privacy queasy, the concepts used in the demo aren't ready for "Little Brother" yet. "If you asked me if I could go out into the streets of New York and identify anyone and everyone, the answer is no," Acquisti says.

That's because the off-the-shelf system that the researchers used won't scale to a task of that magnitude. "If you wanted to identify anyone in the street of a large city, you'd need a database of hundreds of millions of people, and--given the computational power available now--it's still not possible to do these face match-ups in real time," Acquisti explains.