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Interaction data are identifiable even across long periods of time
Fine-grained records of people’s interactions, both offline and online, are collected at 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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8789822/ https://www.ncbi.nlm.nih.gov/pubmed/35078995 http://dx.doi.org/10.1038/s41467-021-27714-6 |
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author | Creţu, Ana-Maria Monti, Federico Marrone, Stefano Dong, Xiaowen Bronstein, Michael de Montjoye, Yves-Alexandre |
author_facet | Creţu, Ana-Maria Monti, Federico Marrone, Stefano Dong, Xiaowen Bronstein, Michael de Montjoye, Yves-Alexandre |
author_sort | Creţu, Ana-Maria |
collection | PubMed |
description | Fine-grained records of people’s interactions, both offline and online, are collected at 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. Our results suggest that people with well-balanced interaction graphs are more identifiable. Applying our attack to Bluetooth close-proximity networks, we show that even 1-hop interaction graphs are enough to identify people more than 26% of the time. Our results provide strong evidence that disconnected and even re-pseudonymized interaction data can be linked together making them personal data under the European Union’s General Data Protection Regulation. |
format | Online Article Text |
id | pubmed-8789822 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-87898222022-02-07 Interaction data are identifiable even across long periods of time Creţu, Ana-Maria Monti, Federico Marrone, Stefano Dong, Xiaowen Bronstein, Michael de Montjoye, Yves-Alexandre Nat Commun Article Fine-grained records of people’s interactions, both offline and online, are collected at 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. Our results suggest that people with well-balanced interaction graphs are more identifiable. Applying our attack to Bluetooth close-proximity networks, we show that even 1-hop interaction graphs are enough to identify people more than 26% of the time. Our results provide strong evidence that disconnected and even re-pseudonymized interaction data can be linked together making them personal data under the European Union’s General Data Protection Regulation. Nature Publishing Group UK 2022-01-25 /pmc/articles/PMC8789822/ /pubmed/35078995 http://dx.doi.org/10.1038/s41467-021-27714-6 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Creţu, Ana-Maria Monti, Federico Marrone, Stefano Dong, Xiaowen Bronstein, Michael de Montjoye, Yves-Alexandre Interaction data are identifiable even across long periods of time |
title | Interaction data are identifiable even across long periods of time |
title_full | Interaction data are identifiable even across long periods of time |
title_fullStr | Interaction data are identifiable even across long periods of time |
title_full_unstemmed | Interaction data are identifiable even across long periods of time |
title_short | Interaction data are identifiable even across long periods of time |
title_sort | interaction data are identifiable even across long periods of time |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8789822/ https://www.ncbi.nlm.nih.gov/pubmed/35078995 http://dx.doi.org/10.1038/s41467-021-27714-6 |
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