After the New England Patriots earned another trip the to Super Bowl, CBS aired the premier of Hunted.  For those who are unfamiliar with the show’s premise, “Hunted follows nine teams of two in a real-life manhunt as they attempt the nearly impossible task of disappearing in today’s vast digital world as highly skilled investigators combine state-of-the-art tracking methods with traditional tactics to pursue and catch them. From searching their targets’ homes and scouring their internet and cell phone histories, to identifying behavioral patterns, Hunters in the field and Command Center investigators work together to identify clues to potential hiding places and collaborators that can ultimately lead to capture.”

What’s fascinating to me about the show isn’t the game, it’s the technology available to both the law enforcement teams and the contestants that make it virtually impossible to escape traceability.  It’s not only the tools at the disposal of law enforcement but also the tremendous digital footprint we generate continuously when we possess operating computing and mobile devices.

Last evening I read in Bruce Schneier’s book Data and Goliath, that by using public data from the 1990 census, a computer scientist found that 87% of the U.S. population could likely be uniquely identified by their five-digit ZIP code combined with their gender and date of birth.  We’re not talking deep dark personal data here.  Upon reflection, I guess this shouldn’t surprise me too much. According the the US Postal Service, there are 7,489 people in the average zip code, so the odds of one of 7,489 people being male and having my birth date are really small.  What does fascinate me is the means to store and process the data so cheaply enables a data scientist with various lists to identify me.

Does the sharing economy use this type of data and analysis?  Absolutely.  In fact, it relies on it.