ID theft possible using public data from social networks
Tuesday, July 7, 2009
Social Security numbers can be predicted with high accuracy from an individual's state and date of birth - information that is often publicly available on social networking sites - raising the risk of identity theft, researchers have found.
Alessandro Acquisti, a professor of information technology and public policy at Carnegie Mellon, and a fellow researcher used data from the Social Security Administration's Death Master File to detect statistical patterns to predict SSNs.
Combined with information from public databases or social networks, the prediction model could determine SSNs with alarming ease.
"In a world of wired consumers, it is possible to combine information from multiple sources to infer data that is more personal and sensitive than any single piece of original information alone," Acquisti said.
The researchers were able to identify all nine SSN digits for 8.5 percent of individuals born after 1988 in fewer than 1,000 attempts. They identified the first five SSN digits of 44 percent of individuals born after 1988 in a single attempt.
A fraudster who knows just the first five digits of an individual's SSN might use a phishing email to trick the person into revealing the last four digits. Botnets could be used to repeatedly apply for credit cards in a person's name until hitting the correct nine-digit sequence.
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