{"id":517892,"date":"2022-04-08T08:14:33","date_gmt":"2022-04-08T12:14:33","guid":{"rendered":"https:\/\/www.rochester.edu\/newscenter\/?p=517892"},"modified":"2022-04-13T17:34:38","modified_gmt":"2022-04-13T21:34:38","slug":"a-cautionary-tale-about-location-tracking-517892","status":"publish","type":"post","link":"https:\/\/www.rochester.edu\/newscenter\/a-cautionary-tale-about-location-tracking-517892\/","title":{"rendered":"A \u2018cautionary tale\u2019 about location tracking"},"content":{"rendered":"

New research out of the Ä¢¹½´«Ã½ shows that data collected from your acquaintances and even strangers can predict your location.<\/h2>\n

Data about our habits and movements are constantly collected via mobile phone apps, fitness trackers, credit card logs, websites visited, and other means.<\/p>\n

But if we turn off data tracking on our devices, aren\u2019t we untraceable?<\/p>\n

No, according to a new study.<\/p>\n

\u201cSwitching off your location data is not going to entirely help,\u201d says Gourab Ghoshal<\/a>, an associate professor of physics, mathematics, and computer science and the Stephen Biggar \u201992 and Elizabeth Asaro\u00a0\u201992 Fellow in Data Science\u00a0at the Ä¢¹½´«Ã½<\/a>.<\/p>\n

Ghoshal, joined by 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 find out just how far-reaching a person\u2019s data might be. The researchers discovered that even if individual users turned off data tracking and didn\u2019t share their own information, their mobility patterns could still be predicted with surprising accuracy based on data collected from their acquaintances.<\/p>\n

\u201cWorse,\u201d says Ghoshal, \u201calmost as much latent information can be extracted from perfect strangers that the individual tends to co-locate with.\u201d<\/p>\n

The researchers published their findings in Nature Communications<\/em><\/a>.<\/p>\n

The smoking gun: your \u2018colocation network\u2019<\/strong><\/h3>\n

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 nearly 36,000 anonymous users.<\/p>\n

They developed a \u201ccolocation\u201d network to distinguish between the mobility patterns of two sets of people:<\/p>\n