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Breaking Stealth (or, I Can Name That Algo in Four Notes)

Traders Magazine Online News, May 10, 2018

Jeff Alexander and Linda Giordano

If I know four of your credit card transactions, I know all of your credit card transactions.  A scary but true statement based on a research paper titled “Unique in the shopping mall: On the Re-identifiability of Credit Card Metadata.”[1]  Another study, “Health Data in an Open World”[2], discussed the unintended re-identification of patients in a heavily masked dataset of 2.9 million Australian citizens that was made public with the goal of transparency for the communal good.

In the former instance, a group of researchers developed an algorithm that can identify an individuals’ credit card transactions with 90% accuracy from a database of masked purchases transacted by 1.1 million people--by only knowing four transactions.  The researchers didn’t even have full transaction data, but only location and date.  When relatively wide price ranges were added, the accuracy increased to 94%.

From just this skimpy amount of input, these analysts were able to pick out a single consumer’s activity across tens of millions of masked transactions in this data set.

The ability to identify a user in a larger, anonymized dataset is called unicity.  The higher the unicity of a data set is, the easier it is to identify that user.   The unicity of a credit card data set is extremely high.  In this blog post, we will do a basic exploration of the unicity of algorithmic trading.

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