Differential privacy is a safeguard used to protect an individual’s data privacy. It allows for the collection and publication of data patterns and trends, while protecting the privacy of individuals captured in a dataset. Differential privacy is not a tool or method, but rather a criterion or a property that multiple methods can achieve. More specifically, it is a mathematical definition of privacy that quantifies privacy risk. 

As the world confronts systematic, interrelated challenges from a raging pandemic to devastating climate catastrophes to a growing chasm of inequality, the United States has the opportunity to make deep commitments to new technological foundations that will usher in the next industrial revolution and greater shared prosperity. Or, we can continue along a business-as-usual path, ceding global leadership and the associated economic value creation elsewhere.