Report: Statistical techniques can help predict Social Security numbers

Report: Statistical techniques can help predict Social Security numbers

Going by an article published in ‘The Proceedings of the National Academy of Sciences’ on Monday, researchers at Carnegie Mellon University claim that loopholes in the country’s Social Security numbering system make millions of citizens susceptible to privacy breaches.

The researchers, who made use of statistical techniques for predicting Social Security numbers (SSNs), on the basis of an individual’s date and location of birth, came to the conclusion that privacy safeguards – that have been set during times time when powerful computers and omnipresent networks were not so frequently used - are increasingly being rendered ineffective.

Saying that the eons-old privacy safeguards set up an “architecture of vulnerability” around personal digital information, the researchers said that the ability to apply statistic deduction for predicting SSNs exposes them to “mass scale” identity fraud risks.

For determining the statistical patterns involved in the assignment of numbers, the researchers examined the SSNs of people who have died; and used the thus-acquired patterns to predict a living person’s Social Security number. 

In the first attempt, the researchers successfully matched the first five Social Security digits for 44 percent of people listed as dead from between 1989 and 2003; and for 7 percent of people born between 1973 and 1988.

The article said that the above findings clearly “imply the potential identification of millions of SSNs for individuals whose birth data were available.”

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