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

2015; American Association for the Advancement of Science; Volume: 347; Issue: 6221 Linguagem: Inglês

10.1126/science.1256297

1095-9203

Yves-Alexandre de Montjoye, Laura Radaelli, Vivek K. Singh, Alex Pentland,

Privacy, Security, and Data Protection

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.