Unique in the shopping mall: On the reidentifiability of credit card metadata. Montjoye, Y. d.; Radaelli, L.; Singh, V. K.; and Pentland, A. “. Science, 347(6221):536–539, January, 2015.
Unique in the shopping mall: On the reidentifiability of credit card metadata [link]Paper  doi  abstract   bibtex   
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.
@article{montjoye_unique_2015,
	title = {Unique in the shopping mall: {On} the reidentifiability of credit card metadata},
	volume = {347},
	copyright = {Copyright © 2015, American Association for the Advancement of Science},
	issn = {0036-8075, 1095-9203},
	shorttitle = {Unique in the shopping mall},
	url = {https://science.sciencemag.org/content/347/6221/536},
	doi = {10.1126/science.1256297},
	abstract = {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.},
	language = {en},
	number = {6221},
	urldate = {2020-02-21},
	journal = {Science},
	author = {Montjoye, Yves-Alexandre de and Radaelli, Laura and Singh, Vivek Kumar and Pentland, Alex “Sandy”},
	month = jan,
	year = {2015},
	pmid = {25635097},
	pages = {536--539}
}
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