A neural network learned from individuals’ weekly social interactions and used that information to single them out of a large, supposedly anonymous dataset.
de Montjoye and his colleagues hypothesized that people’s social behavior could be used to pick them out of datasets containing information on anonymous users’ interactions. To test their hypothesis, the researchers taught an artificial neural network — an AI that simulates the neural circuitry of a biological brain — to recognize patterns in users’ weekly social interactions.
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.
To see whether the AI could profile social behavior elsewhere, the researchers tested it on a dataset consisting of four weeks of close-proximity data from the mobile phones of 587 anonymous university students, collected by researchers in Copenhagen. This included interaction data consisting of students’ pseudonyms, encounter times and the strength of the received signal, which was indicative of proximity to other students.
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