Implementing Differential Privacy for the 2020 Census

Monday, February 01, 2021 - 12:30 pm1:00 pm

Simson Garfinkel, US Census Bureau

Abstract: 

Differential Privacy was invented in 2006 to protect the privacy of people who respond to a national census. The U.S. 2020 Census of Population and Housing will mark the first time that differential privacy will be used for its existential purpose. Bringing leading-edge privacy technology from the lab into practice required a significant amount of scientific and technical development, and it presented organizational challenges as well to one of the world’s largest statistical organization. Nevertheless, in three years the Census Bureau assembled a team, developed a reference implementation, transitioned that implementation to Amazon Web Services, redesigned the implementation’s framework, creating a system that made it easy to perform experiments, performed an end-to-end test, used the implementation to re-release data from the 2010 Census, developed new algorithms to address the data quality concerns of stakeholders, and released multiple reference implementations of the code base.

Simson Garfinkel, US Census Bureau

Simson L. Garfinkel is the Senior Computer Scientist for Confidentiality and Data Access at the US Census Bureau. He has published research articles in the areas of computer security, digital forensics and privacy. He is a fellow of both the Association for Computing Machinery and the Institute for Electrical and Electronics Engineers, and was awarded a PhD in Computer Science from MIT in 2005. He was previously an Associate Professor at the Naval Postgraduate School and a Computer Scientist at the National Institute of Standards and Technology.

BibTeX
@conference {264108,
author = {Simson Garfinkel},
title = {Implementing Differential Privacy for the 2020 Census},
year = {2021},
address = {Oakland, CA},
publisher = {{USENIX} Association},
month = feb,
}