On Differential Privacy ➝

Andy Greenberg, writing for Wired:

Differential privacy, translated from Apple-speak, is the statistical science of trying to learn as much as possible about a group while learning as little as possible about any individual in it. With differential privacy, Apple can collect and store its users’ data in a format that lets it glean useful notions about what people do, say, like and want. But it can’t extract anything about a single, specific one of those people that might represent a privacy violation. And neither, in theory, could hackers or intelligence agencies.

I know, basically nothing about differential privacy, but it sure seems like the solution to Apple’s privacy versus big-data-based machine learning problem.