Your Social Security number is your personal code contained in many important documents: medical records, insurance files, job applications, banking accounts and college applications, to name a few.
This nine-digit number, however, might not be as personal as some people think, creating a potential increased risk for identity theft.
In a 2009 study by Alessandro Acquisti an Ralph Gross, two researchers at Carnegie Mellon, it was determined that Social Security numbers can be predicted based on a person’s date and state of birth.
The two researchers analyzed a database owned by the federal government called the Social Security Administration’s Death Master File. This file contains Social Security numbers, dates of birth and death and states of birth of the deceased. It was created so that someone could not assume a person’s identity after they died.
The researchers recognized statistical patterns based on geographic location and date of birth of the deceased and found that they could predict the Social Security numbers of the living.
By no means could the researchers guess all of Social Security numbers they attempted, but their percent of correct predictions increased in smaller states and with more recent birth dates.
Potentially all someone needs to assume someone’s identity is a birth date, birth state, some patience and trial and error.
That’s a frightening thought as those two simple pieces of information can be found with little effort.
Social networking sites display that information in many case and I do not expect that it would be hard to look someone up in public records either.
What I don’t understand is why the government doesn’t use a randomized number system. Did they not expect that someone could outsmart their system and notice patterns such as assigning numbers based on birth date and geographic location?
According to the study, the two researchers concluded that the government must reevaluate their method of assigning Social Security numbers to reduce the predictability of people’s Social Security numbers.