“With a large dataset that consists of records of individuals, you might like to run a machine learning algorithm to derive statistical insights from the database as a whole, but you want to prevent some outside observer or attacker from learning anything specific about some [individual] in the data set,” says Aaron Roth, a University of Pennsylvania computer science professor whom Apple’s Federighi named in his keynote as having “written the book” on differential privacy. (That book, co-written with Microsoft researcher Cynthia Dwork, is the Algorithmic Foundations of Differential Privacy [PDF].) “Differential privacy lets you gain insights from large datasets, but with a mathematical proof that no one can learn about a single individual.”
Apple will be collecting more data in iOS 10 but it seems like they’re taking an approach that protects user data while still keeping up with what every other big-data company is doing.