Since it was introduced in 2006 by theoretical computer scientists Dwork, McSherry, Nissim, and Smith, differential privacy has become the leading framework for ensuring that individual-level information is not leaked through statistical releases or machine learning models built from sensitive datasets. In addition to a rich theoretical literature, differential privacy has also started to make the transition to practice, with large-scale applications by the US Census Bureau and technology companies like Google, Apple, Microsoft, and Meta.

In this talk, I will describe OpenDP, a community effort to advance the practice of differential privacy, in part by building a trustworthy and open-source suite of differential privacy tools that can be easily adopted by custodians of sensitive data to make it available for research and exploration in the public interest. We aim for OpenDP to foster not only the development of the technology but also its responsible and appropriate use, and I look forward to a productive discussion on how we can do so.