New research has suggested that posting personal information on social networking sites like Facebook may not be as safe as most users think.
According to a report by Forbes, research at Carnegie Mellon University (CMU) shows that one could find out sensitive information including Social Security numbers, Google profile, LinkedIn work history, and credit report by combining offline and online resources.
In the study, researchers from the CMU successfully matched Facebook users with their pictures on other accounts that have anonymous profiles.
"We took unidentified profile photos from a popular dating site (where people use pseudonyms to protect privacy), compared them – using face recognition – to identified photos from social networking sites (namely, we used what of a Facebook profile can be publicly accessed via a search engine; we did not even log on to the network itself), and ended up re-identifying a statistically significant proportion of members of the dating site," said the researchers about the online-to-online re-identification experiment.
In the offline-to-online re-identification, the researchers focused on re-identifying students on the campus of a North American college.
They said, "We took images of them with a webcam and then compared those shots to images from Facebook profiles. Using this approach, we re-identified about one third of the subjects in the experiment."
They could also guess quite successfully about individuals’ Social Security numbers and then find Google profile, LinkedIn work history, credit report, and other private information of users.
"If an individual’s face in the street can be identified using a face recognizer and identified images from social network sites such as Facebook or LinkedIn, then it becomes possible not just to identify that individual, but also to infer additional, and more sensitive, information about her, once her name has been (probabilistically) inferred," the researchers said.
The National Science Foundation and the US Army Research Office funded the research along with support from the Carnegie Mellon Berkman Fund, Heinz College, and CyLab.
