Webinar: https://www.youtube.com/watch?v=cOu-sTV8J2M

This chapter explains how administrative data containing personal information can be collected, analyzed, and published in a way that ensures the individuals in the data will be afforded the strong protections of differential privacy.

It is intended as a practical resource for government agencies and research organizations interested in exploring the possibility of implementing tools for differentially private data sharing and analysis. Using intuitive examples rather than the mathematical formalism used in other guides, this chapter introduces the differential privacy definition and the risks it was developed to address. The text employs modern privacy frameworks to explain how to determine whether the use of differential privacy is an appropriate solution in a given setting. It also discusses the design considerations one should take into account when implementing differential privacy. This discussion incorporates a review of real-world implementations, including tools designed for tiered access systems combining differential privacy with other disclosure controls presented in this Handbook, such as consent mechanisms, data use agreements, and secure environments.

Differential privacy technology has passed a preliminary transition from being the subject of academic work to initial implementations by large organizations and high-tech companies that have the expertise to develop and implement customized differentially private methods. With a growing collection of software packages for generating differentially private releases from summary statistics to machine learning models, differential privacy is now transitioning to being usable more widely and by smaller organizations.