Apple: Learning with Privacy at Scale

Gaining insight into the overall user population is crucial to improving the user experience. The data needed to derive such insights is personal and sensitive, and must be kept private. In addition to privacy concerns, practical deployments of learning systems using this data must also consider resource overhead, computation costs, and communication costs. In this article, we give an overview of a system architecture that combines differential privacy and privacy best practices to learn from a user population.

A new article from Apple’s Machine Learning Journal, which includes a link to a PDF with in-depth equations and other details.