From 2016 through 2021, statisticians and computer scientists at the US Census Bureau worked on the largest and most complex deployment of differential privacy to date: using the modern mathematics of privacy to protect the census responses for more than 330 million residents of the United States as part of the 2020 Census of Population and Housing.
This talk presents a first-hand account of the challenges that were faced trying to apply the still young and evolving theory of differential privacy to the world’s longest running statistical program. These challenges included the need to complete and deploy scientific research on a tight deadline, working in complex deployment environments that had been intentionally crippled to achieve cybersecurity goals, working with a hostile data community of data users who did want formal privacy protections applied to census data, and periodic interference from state and federal officials.
This talk also include a brief introduction to differential privacy and a guide to the growing literature and data products that the 2020 Census produced.
https://us02web.zoom.us/j/85815949834?pwd=NjFPSzRTWFVzYXkwUm02Z3N2QW5LUT09
Meeting ID: 858 1594 9834
Passcode: 904979
RECORDING : Please note, this event will be recorded and will be available after the event for an indeterminate period under a CC BY -NC-ND license. Audience members should bear this in mind before joining the webinar or asking questions.
