Okay, so the National Security Agency is sitting on a treasure trove of all your metadata. What exactly can they learn about you from something as vague as the time and duration of your calls?
While the government can get your Verizon cell data in a breeze, it is naturally much harder to get ahold of it yourself. Stein wrote a Ruby script to mine his own metadata from his Google Voice account. His goal: to figure out whether he could identify the gender of a caller based solely on the time of day of a call and how long it lasted.
He pulled 20 random phone numbers from his call history and marked whether they belonged to a man or a woman. Then he used all the calls from those 20 numbers as his test samples, including the time and duration of call. Google’s Prediction API gave his model a 67 percent confidence level in predicting the gender of a caller after training with those 861 test examples. Though by scientific terms, that’s not particularly accurate, Stein “found it surprisingly good at determining a caller’s gender.”
As he points out, his results might be skewed by his small, individually-specific sample, but it’s a testament to exactly how much you could find out about a person with even more specific algorithms and access to a huge amounts of data:
For aspiring spooks, he’s taking suggestions on how he should spy on himself next here.
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