What is differential privacy?

Differential privacy is a system for publicly sharing information about a dataset by describing the patterns of groups within the dataset while withholding information about individuals in the dataset.

Differential privacy can be achieved by a number of methods, two of the most common are adding randomized “noise” and/or applying redaction thresholds to an aggregate query result to protect individual entries … all without significantly changing the result.

As privacy regulations continue to evolve, differential privacy makes it possible for tech companies to collect and share aggregate information about user habits, while maintaining the privacy of individuals. This is a huge win for both brands and consumers.

Google was first to put the method to commercial use, in 2014, with RAPPOR: its tool for studying Chrome user data without endangering privacy; Apple’s first use followed in 2016.

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