Data Privacy Protection and the Conduct of Applied Research
Methods, Approaches, and New Findings
Data Privacy Protection and the Conduct of Applied Research
Methods, Approaches, and New Findings
A necessary exploration into the mechanics of data privacy protections within empirical social science research.
The explosion of computational power and data availability has revolutionized empirical social science research, but it has also created unprecedented challenges for protecting the privacy of individuals and businesses. As computational tools make it increasingly possible to breach data anonymity in government surveys by combining the survey data with additional information from external sources, statistical agencies face mounting pressure to develop new privacy protection methods while maintaining data quality essential for research and policymaking. This volume explores how innovations in data privacy protection, including differential privacy and synthetic data methods, affect the conduct of empirical analysis in economics, computer science, and statistics. Contributors explore critical questions about the trade-offs between privacy and data usability: How do new protection methods impact statistical inference and parameter estimation? What standards should data providers adopt? The chapters examine frameworks for characterizing privacy protection, disclosure limitation challenges for survey data, methodological innovations for privacy-preserving statistical analysis, regulatory considerations in modern data governance, and strategies for balancing confidentiality with research access.
This volume provides researchers, statistical agencies, and policymakers with essential guidance for navigating the complex landscape where data protection meets scientific inquiry.
288 pages | 9 halftones, 16 line drawings, 6 tables | 6 x 9
National Bureau of Economic Research Conference Report
Economics and Business: Economics--Econometrics and Statistics
Table of Contents
Acknowledgments
Introduction
V. Joseph Hotz and Ruobin Gong
I. Data Privacy Protection: New Frameworks and Standards
1. A Secure Query System to Improve Access to Individual Income Tax Data
Amy O’Hara, Stephanie Straus, Ron Borzekowski, Paul Arnsberger, and Barry Johnson
2. The Case for Researching Applied Privacy-Enhancing Technologies
Claire McKay Bowen, Joshua Snoke, Aaron R. Williams, and Andrés F. Barrientos
3. Using Containers for Analysis Validation at Scale
Lars Vilhuber
4. Differential Privacy Meets Invariant Statistics: Some Conundrums in Quantifying Trade-Offs
James Bailie, Ruobin Gong, and Xiao-Li Meng
II. Privacy Protection in Survey Research
5. The Complexities of Differential Privacy for Survey Data
Jörg Drechsler and James Bailie
6. Differentially Private Population Quantity Estimates via Survey Weight Regularization
Jeremy Seeman, Yajuan Si, and Jerome P. Reiter
7. Synthetic Data and Social Science Research: Accuracy Assessments and Practical Considerations from the SIPP Synthetic Beta
Jordan Stanley and Evan S. Totty
8. Considerations for Developing Disclosure Avoidance Systems for Longitudinal Survey Data
V. Joseph Hotz and Trivellore Raghunathan
III. Privacy-Preserving Data Analysis
9. Improving Privacy for Respondents in Randomized Controlled Trials: A Differential Privacy Approach
Soumya Mukherjee, Aratrika Mustafi, Aleksandra Slavković, and Lars Vilhuber
10. A Simulation-Based Method to Estimating Economic Models with Privacy-Protected Data
Jung Sakong and Alexander K. Zentefis
11. Data Privacy for Record Linkage and Beyond
Shurong Lin and Eric D. Kolaczyk
IV. Privacy Policy and Data Governance
12. The Realities of Disclosure Risks in the Age of Dark Patterns and Big Data
Ramon Abraham A. Sarmiento
13. Effective Regulation and Firm Compliance: The Case of German Privacy Policies
Jacopo Gambato, Bernhard Ganglmair, and Julia Krämer
14. Navigating the Privacy Landscape: Harmonizing Legislative and Public Sector Approaches in the Canadian Context
Pierre Desrochers and Eric Rancourt
V. Balancing Data Privacy and Data Usability
15. Privacy Protection and Accuracy: What Do We Know? Do We Know Things?? Let’s Find Out!
Evan S. Totty and Thor Watson
16. The Importance of Confidential Microdata for Economic Research
Abhishek Nagaraj, Fernando Stipanicic, and Matteo Tranchero
17. Privacy Elasticity: Modeling Responses to Quantifiable Privacy Changes
Inbal Dekel, Rachel Cummings, Ori Heffetz, and Katrina Ligett