Unique in the shopping mall: On the reidentifiability of credit card metadata

  • Yves-Alexandre de Montjoye
    Media Lab, Massachusetts Institute of Technology (MIT), 20 Amherst Street, Cambridge, MA 02139, USA.
  • Laura Radaelli
    Department of Computer Science, Aarhus University, Aabogade 34, Aarhus, 8200, Denmark.
  • Vivek Kumar Singh
    Media Lab, Massachusetts Institute of Technology (MIT), 20 Amherst Street, Cambridge, MA 02139, USA.
  • Alex “Sandy” Pentland
    Media Lab, Massachusetts Institute of Technology (MIT), 20 Amherst Street, Cambridge, MA 02139, USA.

書誌事項

公開日
2015-01-30
権利情報
  • http://www.sciencemag.org/about/science-licenses-journal-article-reuse
DOI
  • 10.1126/science.1256297
公開者
American Association for the Advancement of Science (AAAS)

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説明

<jats:p>Large-scale data sets of human behavior have the potential to fundamentally transform the way we fight diseases, design cities, or perform research. Metadata, however, contain sensitive information. Understanding the privacy of these data sets is key to their broad use and, ultimately, their impact. We study 3 months of credit card records for 1.1 million people and show that four spatiotemporal points are enough to uniquely reidentify 90% of individuals. We show that knowing the price of a transaction increases the risk of reidentification by 22%, on average. Finally, we show that even data sets that provide coarse information at any or all of the dimensions provide little anonymity and that women are more reidentifiable than men in credit card metadata.</jats:p>

収録刊行物

  • Science

    Science 347 (6221), 536-539, 2015-01-30

    American Association for the Advancement of Science (AAAS)

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