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Unique in the Crowd: The privacy bounds of human mobility

We study fifteen months of human mobility data for one and a half million individuals and find that human mobility traces are highly unique. In fact, in a dataset where the location of an individual is specified hourly, and with a spatial resolution equal to that given by the carrier's antennas...

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Detalles Bibliográficos
Autores principales: de Montjoye, Yves-Alexandre, Hidalgo, César A., Verleysen, Michel, Blondel, Vincent D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3607247/
https://www.ncbi.nlm.nih.gov/pubmed/23524645
http://dx.doi.org/10.1038/srep01376
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author de Montjoye, Yves-Alexandre
Hidalgo, César A.
Verleysen, Michel
Blondel, Vincent D.
author_facet de Montjoye, Yves-Alexandre
Hidalgo, César A.
Verleysen, Michel
Blondel, Vincent D.
author_sort de Montjoye, Yves-Alexandre
collection PubMed
description We study fifteen months of human mobility data for one and a half million individuals and find that human mobility traces are highly unique. In fact, in a dataset where the location of an individual is specified hourly, and with a spatial resolution equal to that given by the carrier's antennas, four spatio-temporal points are enough to uniquely identify 95% of the individuals. We coarsen the data spatially and temporally to find a formula for the uniqueness of human mobility traces given their resolution and the available outside information. This formula shows that the uniqueness of mobility traces decays approximately as the 1/10 power of their resolution. Hence, even coarse datasets provide little anonymity. These findings represent fundamental constraints to an individual's privacy and have important implications for the design of frameworks and institutions dedicated to protect the privacy of individuals.
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spelling pubmed-36072472013-03-25 Unique in the Crowd: The privacy bounds of human mobility de Montjoye, Yves-Alexandre Hidalgo, César A. Verleysen, Michel Blondel, Vincent D. Sci Rep Article We study fifteen months of human mobility data for one and a half million individuals and find that human mobility traces are highly unique. In fact, in a dataset where the location of an individual is specified hourly, and with a spatial resolution equal to that given by the carrier's antennas, four spatio-temporal points are enough to uniquely identify 95% of the individuals. We coarsen the data spatially and temporally to find a formula for the uniqueness of human mobility traces given their resolution and the available outside information. This formula shows that the uniqueness of mobility traces decays approximately as the 1/10 power of their resolution. Hence, even coarse datasets provide little anonymity. These findings represent fundamental constraints to an individual's privacy and have important implications for the design of frameworks and institutions dedicated to protect the privacy of individuals. Nature Publishing Group 2013-03-25 /pmc/articles/PMC3607247/ /pubmed/23524645 http://dx.doi.org/10.1038/srep01376 Text en Copyright © 2013, Macmillan Publishers Limited. All rights reserved http://creativecommons.org/licenses/by-nc-nd/3.0/ This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/3.0/
spellingShingle Article
de Montjoye, Yves-Alexandre
Hidalgo, César A.
Verleysen, Michel
Blondel, Vincent D.
Unique in the Crowd: The privacy bounds of human mobility
title Unique in the Crowd: The privacy bounds of human mobility
title_full Unique in the Crowd: The privacy bounds of human mobility
title_fullStr Unique in the Crowd: The privacy bounds of human mobility
title_full_unstemmed Unique in the Crowd: The privacy bounds of human mobility
title_short Unique in the Crowd: The privacy bounds of human mobility
title_sort unique in the crowd: the privacy bounds of human mobility
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3607247/
https://www.ncbi.nlm.nih.gov/pubmed/23524645
http://dx.doi.org/10.1038/srep01376
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