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Privacy challenges—Analysis: It’s surprisingly easy to identify individuals from credit-card metada…
http://newsoffice.mit.edu/2015/identify-from-credit-card-metadata-0129[font face=Serif][font size=5]Privacy challenges[/font]
[font size=4]Analysis: It’s surprisingly easy to identify individuals from credit-card metadata.[/font]
Larry Hardesty | MIT News Office
January 29, 2015
[font size=3]In this week’s issue of the journal Science, MIT researchers report that just four fairly vague pieces of information — the dates and locations of four purchases — are enough to identify 90 percent of the people in a data set recording three months of credit-card transactions by 1.1 million users.
When the researchers also considered coarse-grained information about the prices of purchases, just three data points were enough to identify an even larger percentage of people in the data set. That means that someone with copies of just three of your recent receipts — or one receipt, one Instagram photo of you having coffee with friends, and one tweet about the phone you just bought — would have a 94 percent chance of extracting your credit card records from those of a million other people. This is true, the researchers say, even in cases where no one in the data set is identified by name, address, credit card number, or anything else that we typically think of as personal information.
The paper comes roughly two years after an earlier analysis of mobile-phone records that yielded very similar results.
“If we show it with a couple of data sets, then it’s more likely to be true in general,” says Yves-Alexandre de Montjoye, an MIT graduate student in media arts and sciences who is first author on both papers. “Honestly, I could imagine reasons why credit-card metadata would differ or would be equivalent to mobility data.”
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http://dx.doi.org/10.1126/science.1256297[font size=4]Analysis: It’s surprisingly easy to identify individuals from credit-card metadata.[/font]
Larry Hardesty | MIT News Office
January 29, 2015
[font size=3]In this week’s issue of the journal Science, MIT researchers report that just four fairly vague pieces of information — the dates and locations of four purchases — are enough to identify 90 percent of the people in a data set recording three months of credit-card transactions by 1.1 million users.
When the researchers also considered coarse-grained information about the prices of purchases, just three data points were enough to identify an even larger percentage of people in the data set. That means that someone with copies of just three of your recent receipts — or one receipt, one Instagram photo of you having coffee with friends, and one tweet about the phone you just bought — would have a 94 percent chance of extracting your credit card records from those of a million other people. This is true, the researchers say, even in cases where no one in the data set is identified by name, address, credit card number, or anything else that we typically think of as personal information.
The paper comes roughly two years after an earlier analysis of mobile-phone records that yielded very similar results.
“If we show it with a couple of data sets, then it’s more likely to be true in general,” says Yves-Alexandre de Montjoye, an MIT graduate student in media arts and sciences who is first author on both papers. “Honestly, I could imagine reasons why credit-card metadata would differ or would be equivalent to mobility data.”
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