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Interaction data are identifiable even across long periods of time
Ana-Maria Cretu · Federico Monti · Stefano Marrone · Xiaowen Dong · Michael Bronstein · Yves-Alexandre Montjoye

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in Privacy in Machine Learning (PriML) 2021 »
Event URL: https://openreview.net/forum?id=JA147hzFv-y »
Fine-grained records of people's interactions, both offline and online, are collected at a large scale. These data contain sensitive information about whom we meet, talk to, and when. We demonstrate here how people's interaction behavior is stable over long periods of time and can be used to identify individuals in anonymous datasets. Our attack learns the profile of an individual using geometric deep learning and triplet loss optimization. In a mobile phone metadata dataset of more than 40k people, it correctly identifies 52\% of individuals based on their $2$-hop interaction graph. We further show that the profiles learned by our method are stable over time and that 24\% of people are still identifiable after 20 weeks, thus making identification a real risk in practice. Our results provide strong evidence that disconnected and even re-pseudonymized interaction data can be linked together making them likely to be personal data under the European Union's General Data Protection Regulation.

Author Information

Ana-Maria Cretu (Imperial College London)
Federico Monti (Twitter)
Stefano Marrone (University of Naples Federico II)
Xiaowen Dong (University of Oxford)
Michael Bronstein (Imperial College London / Twitter)
Yves-Alexandre Montjoye

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