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Data collected about individuals is regularly used to make decisions that impact those same individuals.
https://www.indiabudget.gov.in/budget_archive/es2006-07/chapt2007/tab97.pdf
Population of India (1951 - 2001) · 2001
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Fair Representation: Meeting the Ideal of One Man, One Vote (2nd edition)
M. Balinski and H. Peyton Young · 2001
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Stochastic apportionment
G. Grimmett · 2004
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Fairness in apportionment
P. H. Young · 2004
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Calibrating noise to sensitivity in private data analysis
C. Dwork, F. McSherry, K. Nissim, and A. Smith · 2006
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Privacy: Theory meets practice on the map
A. Machanavajjhala, D. Kifer, J. Abowd, J. Gehrke, and L. Vilhuber · 2008
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https://www.census.gov/programs-surveys/decennial-census/2020-census/planning-management/2020-census-data-products/2010-demonstration-data-products.html
2010 demonstration data products, U.S. Census Bureau · 2010
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Probabilistic inference and differential privacy
O. Williams and F. McSherry · 2010
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Fairness through awareness
C. Dwork, M. Hardt, T. Pitassi, O. Reingold, and R. Zemel · 2012
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It’s not privacy, and it’s not fair
C. Dwork and D. K. Mulligan · 2013
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A multidisciplinary survey on discrimination analysis
A. Romei and S. Ruggieri · 2013
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The Algorithmic Foundations of Differential Privacy
C. Dwork and A. Roth · 2014
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Rappor: Randomized aggregatable privacy-preserving ordinal response
Ú. Erlingsson, V. Pihur, and A. Korolova · 2014
Cited alongside, same era.
A data- and workload-aware algorithm for range queries under differential privacy
C. Li, M. Hay, and G. Miklau · 2014
Cited alongside, same era.
Patterns and causes of uncertainty in the american community survey
S. E. Spielman, D. Folch, and N. Nagle · 2014
Cited alongside, same era.
Reducing uncertainty in the american community survey through data-driven regionalization
S. E. Spielman and D. C. Folch · 2015
Cited alongside, same era.
Census Bureau, Voting Rights Act Section 203 Determinations: Statistical methodology summary, November 2016
2016
Cited alongside, same era.
https://www.census.gov/programs-surveys/decennial-census/about/voting-rights/voting-rights-determination-file.html , 2016
Uses of census bureau data in federal funds distribution
M. Hotchkiss and J. Phelan · 2017
Later among the works it cites.
Proceedings from the 2016 NSF–Sloan workshop on practical privacy, Jan 2017
L. Vilhuber and I. M. Schmutte · 2017
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Differentially private bayesian inference for exponential families
G. Bernstein and D. R. Sheldon · 2018
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Privacy for all: Ensuring fair and equitable privacy protections
M. D. Ekstrand, R. Joshaghani, and H. Mehrpouyan · 2018
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Inherent trade-offs in algorithmic fairness
J. Kleinberg · 2018
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Fairness definitions explained
S. Verma and J. Rubin · 2018
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Census Bureau, Voting Rights Determination file · 2016
Cited alongside, same era.
Apple’s ‘differential privacy’ is about collecting your data—but not your
A. Greenberg · 2016
Cited alongside, same era.
Principled evaluation of differentially private algorithms using dpbench
M. Hay, A. Machanavajjhala, G. Miklau, Y. Chen, and D. Zhang · 2016
Cited alongside, same era.
Allocating grants for Title I
W. Sonnenberg · 2016
Cited alongside, same era.
www.census.gov/about/cac/sac/meetings/2017-09-meeting.html, Sep. 2017
Census scientific advisory committee meeting · 2017
Cited alongside, same era.
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
A. Chouldechova · 2017
Cited alongside, same era.
Utility cost of formal privacy for releasing national employer-employee statistics
S. Haney, A. Machanavajjhala, J. Abowd, M. Graham, M. Kutzbach, and L. Vilhuber · 2017
Cited alongside, same era.
Fair division with uncertain needs
J. Xue · 2018
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ϵ \epsilon ktelo: A framework for defining differentially-private computations
D. Zhang, R. McKenna, I. Kotsogiannis, G. Miklau, M. Hay, and A. Machanavajjhala · 2018
Later among the works it cites.
An economic analysis of privacy protection and statistical accuracy as social choices
J. M. Abowd and I. M. Schmutte · 2019
Closest in time.
Differential privacy has disparate impact on model accuracy
E. Bagdasaryan and V. Shmatikov · 2019
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Differentially private bayesian linear regression
G. Bernstein and D. R. Sheldon · 2019
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