Data about our habits and activities is continuously collected through mobile phone apps, fitness trackers, credit card logs, websites visited and other means.
But if we turn off data tracking on our device, can’t we get traced?
No, according to a new study.
“Turning off your location data completely won’t help,” says Gaurav Ghoshal, an associate professor of physics, mathematics and computer science and the Stephen Bigger ’92 and Elizabeth Asaro ’92 Fellow in Data Science at the University of Rochester.
Ghosal, joined with colleagues at the University of Exeter, the Federal University of Rio de Janeiro, Northeastern University and the University of Vermont, applied techniques from information theory and network science to determine how far-reaching an individual’s data could be. The researchers found that even if individual users turned off data tracking and did not share their information, their mobility patterns could be predicted with astonishing accuracy based on data collected from their acquaintances .
Even worse, says Ghoshal, “almost as much secret information can be extracted from complete strangers with whom the person co-locates.”
The researchers published their findings in nature communication,
The Smoking Gun: Your ‘Colocation Network’.
The researchers analyzed four datasets: three location-based social network datasets composed of millions of check-ins on apps such as BrightKite, Facebook and Foursquare, and one call-data record containing more than 22 million calls by about 36,000 anonymous users.
He developed a “colocation” network to differentiate between the mobility patterns of two groups of people:
- people who are socially attached to a person, such as family members, friends, or coworkers
- People who are socially not tied to a person but to a person in one place at the same time. They can include people working in the same building but with different companies, parents whose children attend the same school but who are unknown, or people who shop at the same grocery store.
By applying information theory and measures of entropy — the degree of randomness or structure in a sequence of place visits — researchers learned that people who are socially tied to a person have up to 95 percent of the predictability in their movement patterns. There is information. that person’s mobility pattern. However, even more astonishingly, he found that the stranger No Socially bonded individuals can also provide important information, which predicts up to 85 percent of a person’s movement.
‘A cautionary tale’
The ability to predict the locations of individuals or groups could be beneficial in areas such as urban planning and epidemic control, where contact tracing based on mobility patterns is an important tool for preventing the spread of disease. In addition, many consumers appreciate the potential of data mining to offer tailored recommendations for restaurants, TV shows, and commercials.
However, Ghoshal says, data mining is a slippery slope, especially because, as research has shown, individuals sharing data through mobile apps may inadvertently provide information about others.
“We are presenting a cautionary tale that people should be aware of how far-reaching their data can be,” he says. “This research has a lot of implications for surveillance and privacy issues, especially with the rise of authoritarian impulses. We can’t just tell people to turn off their phones or go off the grid. And it needs to be communicated to put in place guidelines that govern how people collect your data.”
Source: University of Rochester