A Feasibility Study of Differentially Private Summary Statistics and Regression Analyses with Evaluations on Administrative and Survey Data-Reference-Cited by-同舟云学术

A Feasibility Study of Differentially Private Summary Statistics and Regression Analyses with Evaluations on Administrative and Survey Data

Published:2023-10-17 Issue: Volume: Page:1-14
ISSN:0162-1459
Container-title:Journal of the American Statistical Association
language:en
Short-container-title:Journal of the American Statistical Association

Author:

Barrientos Andrés F.¹,Williams Aaron R.²^ORCID,Snoke Joshua³^ORCID,Bowen Claire McKay²^ORCID

Affiliation:

1. Florida State University, Tallahassee, FL

2. Urban Institute, Washington, DC

3. RAND Corporation, Pittsburgh, PA

Funder

Alfred P. Sloan Foundation

National Science Foundation National Center for Science and Engineering Statistics

Publisher

Informa UK Limited

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/01621459.2023.2270795

Reference54 articles.

1. Balle, B., and Wang, Y.-X. (2018), “Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising,” in Proceedings of Machine Learning Research: International Conference on Machine Learning, pp. 394–403.

2. Providing access to confidential research data through synthesis and verification: An application to data on employees of the U.S. federal government

3. Benedetto G. Jordan C. S. and Totty E. (2018) “The Creation and Use of the SIPP Synthetic Beta v7.0 ” US Census Bureau. Available at: https://www.census.gov/content/dam/Census/programs-surveys/sipp/methodology/SSBdescribe_nontechnicalv7.pdf (accessed 26 October 23).

4. “Differentially Private Bayesian Linear Regression,”;Bernstein G.;Advances in Neural Information Processing Systems,2019

5. Politically Feasible Reforms of Nonlinear Tax Systems

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