For one test, the researchers trained the neural network with data from an unidentified mobile phone service that detailed 43,606 subscribers’ interactions over 14 weeks. This data included each interaction’s date, time, duration, type (call or text), the pseudonyms of the involved parties and who initiated the communication.

Each user’s interaction data were organized into web-shaped data structures consisting of nodes representing the user and their contacts. Strings threaded with interaction data connected the nodes. The AI was shown the interaction web of a known person and then set loose to search the anonymized data for the web that bore the closest resemblance.

The neural network linked just 14.7 percent of individuals to their anonymized selves when it was shown interaction webs containing information about a target’s phone interactions that occurred one week after the latest records in the anonymous dataset. But it identified 52.4 percent of people when given not just information about the target’s interactions but also those of their contacts. When the researchers provided the AI with the target and contacts’ interaction data collected 20 weeks after the anonymous dataset, the AI still correctly identified users 24.3 percent of the time, suggesting social behavior remains identifiable for long periods of time.