Over the past decade, calls for better measures to protect sensitive, personally identifiable information have blossomed into what politicians like to call a "hot-button issue." Certainly, privacy violations have become rampant and people have grown keenly aware of just how vulnerable they are. When it comes to potential remedies, however, proposals have varied widely, leading to bitter, politically charged arguments. To date, what has chiefly come of that have been bureaucratic policies that satisfy almost no one—and infuriate many.
Now, into this muddled picture comes differential privacy. First formalized in 2006, it's an approach based on a mathematically rigorous definition of privacy that allows formalization and proof of the guarantees against re-identification offered by a system. While differential privacy has been accepted by theorists for some time, its implementation has turned out to be subtle and tricky, with practical applications only now starting to become available. To date, differential privacy has been adopted by the U.S. Census Bureau, along with a number of technology companies, but what this means and how these organizations have implemented their systems remains a mystery to many.