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How does post-processed differentially private Census data affect redistricting? How concerned should we be about gerrymandering with the new DAS?

Andrew Gelman · published 20 October 2021

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https://www.pnas.org/content/pnas/116/16/7634.full.pdf
pnas.org
https://www.nber.org/papers/w24905
nber.org
https://www.science.org/doi/10.1126/sciadv.abk3283
science.org
https://pubmed.ncbi.nlm.nih.gov/32478311/
pubmed.ncbi.nlm.nih.gov
https://gking.harvard.edu/publications/letter-us-census-bureau-request-release-%E2%80%9Cnoisy-measurements-file%E2%80%9D-september-30
gking.harvard.edu
https://drops.dagstuhl.de/opus/volltexte/2021/13873/pdf/LIPIcs-FORC-2021-5.pdf
drops.dagstuhl.de
https://www.nytimes.com/2018/12/05/upshot/to-reduce-privacy-risks-the-census-plans-to-report-less-accurate-data.html
nytimes.com
https://arxiv.org/pdf/1911.01503.pdf
arxiv.org
https://datasociety.net/library/balancing-data-utility-and-confidentiality-in-the-2020-us-census/
datasociety.net
https://en.wikipedia.org/wiki/Gerrymandering
en.wikipedia.org
https://drive.google.com/file/d/1ov8evbT5bSCiU33UzssFPEkIPfa5uVFw/view
drive.google.com
Elsewhere on statmodeling.stat.columbia.edu (7)
Shots taken, shots returned regarding the Census’ motivation for using differential privacy (and btw, it’s not an algorithm) Read at the source ↗
statmodeling.stat.columbia.edu
https://statmodeling.stat.columbia.edu/wp-content/uploads/2021/10/table1.png
statmodeling.stat.columbia.edu
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statmodeling.stat.columbia.edu
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https://statmodeling.stat.columbia.edu/wp-content/uploads/2021/10/sc_statehouse.png
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https://statmodeling.stat.columbia.edu/wp-content/uploads/2021/10/mmd_densities.png
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