Apple Touts 'Differential Privacy' Data Gathering Technique in iOS 10

With the announcement of iOS 10 at WWDC on Monday, Apple mentioned its adoption of "Differential Privacy" – a mathematical technique that allows the company to collect user information that helps it enhance its apps and services while keeping the data of individual users private.

During the company's keynote address, Senior VP of software engineering Craig Federighi – a vocal advocate of personal privacy – summarized the concept in the following way:

We believe you should have great features and great privacy. Differential privacy is a research topic in the areas of statistics and data analytics that uses hashing, subsampling and noise injection to enable…crowdsourced learning while keeping the data of individual users completely private. Apple has been doing some super-important work in this area to enable differential privacy to be deployed at scale.

Wired has now published an article on the subject that lays out in clearer detail some of the practical implications and potential pitfalls of Apple's latest statistical data gathering technique.

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.

Wired notes that the technique claims to have a mathematically "provable guarantee" that its generated data sets are impervious to outside attempts to de-anonymize the information. It does however caution that such complicated techniques rely on the rigor of their implementation to retain any guarantee of privacy during transmission.

You can read the full article on the subject of differential privacy here.

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