Your Facebook account may reveal the information that you want to keep private, through the friends of your social media contacts, Stanford researchers have found. Researchers found that among people with any online presence, protecting personal data is becoming increasingly difficult.
The study, published in the journal Nature Human Behaviour, shows that there are more ways than previously thought to reveal demographic traits that people might be trying to conceal. Researchers used databases that reflect the kinds of information that websites make available to advertisers or reveal to outside groups when people allow third parties to access their social profiles.
Given the prevalence of such data, the researchers sought to better understand what sorts of statistical inferences might end up revealing traits people have sought to conceal. “In social data, some things are more predictable than others. We set out to study the relationship between friend networks and predictability, and ended up uncovering an inference mechanism that hadn’t been noticed before,” said Johan Ugander, assistant professor at Stanford University in the US.
Researchers, who have studied social media relationships, have found that we tend to friend people of roughly our same age, race and political belief. So even if a person does not reveal their age, race or political views, these traits are easily and accurately inferred from friendship studies. Researchers call this tendency homophily, which stems from the Greek words for love of sameness.
However, not all unknown traits are easy to predict using friend studies. Gender, for instance, exhibits what researchers call weak homophily in online contexts. “If an unknown person in a social network has mostly male friends, there’s an almost equally good chance they could be female, or vice versa,” said Kristen Altenburger, a PhD student at Stanford.
They observe that when there’s monophily in a network, it becomes possible to predict traits of individuals based on friends of friends, even in situations where there’s no homophily. The team relied on standard network datasets widely studied by academics. These datasets map friendship networks and contain complete information about all of the traits of all of the individual traits, including gender.
The researchers then erased the gender data for certain individuals, creating artificial unknowns, and then used their “friends of friends” analysis to see if it could make a prediction. “While we find that your friends don’t tend to predict your gender, the people those friends choose to associate with, your friends of friends, tend to be more similar to you than even your friends are,” Ugander said.
The research highlights the importance of protecting network data from prying hands, researchers said. “We’re not sure what else might be revealed in this way. Unfortunately, it looks like the realm of network privacy is even smaller than we previously thought,” Ugander said.