When Google launched Doodle-4-Google, in which children can compete to design Googleâ€™s homepage logo, they requested contestantsâ€™ Social Security numbers in an effort to prevent duplicate entries.
Americans have become accustomed to handing over the last four digits of their Social Security number as a password or identifier for various accounts and applications. But with the development of new technologies that have cracked the code for the distribution of Social Security numbers, the last four digits have become as sensitive and valuable as the first five.
The coder or marketer at Google who believes itâ€™s reasonable to request the last four digits of childrenâ€™s Social Security numbers is probably someone who readily shares his or her own number, which is not a good idea.
Researchers at Carnegie Mellon University have developed a reliable method to predict Social Security numbers using information from social networking sites, data brokers, voter registration lists, online white pages, and the publicly available Social Security Administrationâ€™s Death Master File.
The New York Times reports, â€œComputer scientists and policy experts say that such seemingly innocuous bits of self-revelation can increasingly be collected and reassembled by computers to help create a picture of a personâ€™s identity, sometimes down to the Social Security numberâ€¦ So far, this type of powerful data mining, which relies on sophisticated statistical correlations, is mostly in the realm of university researchers, not identity thieves and marketers.â€
The primary issue here is new account fraud, or financial identity theft in which the victimâ€™s personally identifiable information and good credit standing are used to create new accounts, which are then used to obtain products and services. Stolen Social Security numbers are often used to commit new account fraud.
Aside from subscribing to an identity theft protection service, itâ€™s difficult to stop or prevent new account fraud. One way that online businesses can mitigate the issue would be to verify the reputation of the computer or smartphone being used to submit credit applications, rather than simply verifying the Social Security number or other identification information provided by credit applicants.
By evaluating a device for criminal history or high risk while its connected to the online site, creditors can automatically detect and reject fraudulent applications. This worked very well for one Fortune 100 credit issuer. A Forrester Consulting Total Economic Impact study found that the device reputation service provided by Oregon-based iovation Inc., identified 43,000 fraudulent credit applications and saved the financial institution $8 million USD over two years in reduced fraud losses and operational efficiencies that their fraud prevention process and team gained.