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University of Cambridge > Talks.cam > Computer Laboratory Security Seminar > Deploying Differential Privacy for the 2020 Census of Population and Housing
Deploying Differential Privacy for the 2020 Census of Population and HousingAdd to your list(s) Download to your calendar using vCal
If you have a question about this talk, please contact Alexander Vetterl. When differential privacy was created more than a decade ago, the motivating example was statistics published by an official statistics agency. In theory there is no difference between theory and practice, but in practice there is. In attempting to transition differential privacy from the theory to practice, and in particular for the 2020 Census of Population and Housing, the U.S. Census Bureau has encountered many challenges unanticipated by differential privacy’s creators. Many of these challenges had less to do with the mathematics of differential privacy and more to do with operational requirements that differential privacy’s creators had not discussed in their writings. These challenges included obtaining qualified personnel and a suitable computing environment, the difficulty of accounting for all uses of the confidential data, the lack of release mechanisms that align with the needs of data users, the expectation on the part of data users that they will have access to micro-data, the difficulty in setting the value of the privacy-loss parameter, ε (epsilon), and the lack of tools and trained individuals to verify the correctness of differential privacy, and push-back from same members of the data user community. Addressing these concerns required developing a novel hierarchical algorithm that makes extensive use of a high-performance commercial optimizer; transitioning the computing environment to the cloud; educating insiders about differential privacy; engaging with academics, data users, and the general public; and redesigning both data flows inside the Census Bureau and some of the final data publications to be in line with the demands of formal privacy. Bio: Simson Garfinkel is the Senior Computer Scientist for Confidentiality and Data Access at the US Census Bureau. He holds seven US patents and has published more than 50 research articles in computer security and digital forensics. He is a fellow of the Association for Computing Machinery (ACM) and the Institute of Electrical and Electronics Engineers (IEEE), and a member of the National Association of Science Writers. His most recent book is The Computer Book, which features 250 chronologically arranged milestones in the history of computing. As a journalist, he has written about science, technology, and technology policy in the popular press since 1983, and has won several national journalism awards. Garfinkel received three Bachelor of Science degrees from MIT in 1987, a Master’s of Science in Journalism from Columbia University in 1988, and a Ph.D. in Computer Science from MIT in 2005. This talk is part of the Computer Laboratory Security Seminar series. This talk is included in these lists:
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