The UK Office for National Statistics (ONS) recently published a methodology report on behalf of the Government Statistical Service (GSS). Members of Privitar’s research and policy teams were invited to contribute a chapter, on an area of privacy engineering we are particularly excited about: differential privacy 1.
Differential privacy has been touted as the solution for accessing all your sensitive data with absolute privacy assurance. The privacy defense that enables you to extract unlimited insights and train AI models while your customer’s hospital visits or pharmacy purchases never get revealed. The reason your compliance team gets out of the way, your data analysis projects start accelerating, and new customers begin lining up with their checkbooks.
But does differential privacy really deliver on these promises?
Unfortunately not. Like any technology innovation, differential privacy (DP) is susceptible to hype: fact and fiction get mixed together in all the excitement. The adoption of DP by technologists at Apple, Google, and the US Census Bureau has only amplified the buzz. The reality is, DP is in its very first stages of being used in the real world, and practitioners are still figuring out when and how to use it.
What are the questions that cut to the heart of any DP discussion, allowing you to distinguish real, immediate value from overhype? This article presents the big three.
Differential privacy leapt from research papers to tech news headlines last year when, in the WWDC keynote, Apple VP of Engineering Craig Federighi announced Apple’s use of the concept to protect user privacy in iOS.