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Privacy and Data-Based Research

Author

Listed:
  • Ori Heffetz
  • Katrina Ligett

Abstract

What can we, as users of microdata, formally guarantee to the individuals (or firms) in our dataset, regarding their privacy? We retell a few stories, well-known in data-privacy circles, of failed anonymization attempts in publicly released datasets. We then provide a mostly informal introduction to several ideas from the literature on differential privacy, an active literature in computer science that studies formal approaches to preserving the privacy of individuals in statistical databases. We apply some of its insights to situations routinely faced by applied economists, emphasizing big-data contexts.

Suggested Citation

  • Ori Heffetz & Katrina Ligett, 2013. "Privacy and Data-Based Research," NBER Working Papers 19433, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:19433
    Note: AG LS PE
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    References listed on IDEAS

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    1. Satkartar K. Kinney & Jerome P. Reiter & Arnold P. Reznek & Javier Miranda & Ron S. Jarmin & John M. Abowd, 2011. "Towards Unrestricted Public Use Business Microdata: The Synthetic Longitudinal Business Database," International Statistical Review, International Statistical Institute, vol. 79(3), pages 362-384, December.
    2. Ori Heffetz & John A. List, 2014. "Is The Endowment Effect An Expectations Effect?," Journal of the European Economic Association, European Economic Association, vol. 12(5), pages 1396-1422, October.
    3. Peter L. Rousseau, 2026. "Report of the Secretary," AEA Papers and Proceedings, American Economic Association, vol. 116, pages 708-713, May.
    4. Wasserman, Larry & Zhou, Shuheng, 2010. "A Statistical Framework for Differential Privacy," Journal of the American Statistical Association, American Statistical Association, vol. 105(489), pages 375-389.
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    Citations

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    Cited by:

    1. Garret Christensen & Edward Miguel, 2018. "Transparency, Reproducibility, and the Credibility of Economics Research," Journal of Economic Literature, American Economic Association, vol. 56(3), pages 920-980, September.
    2. repec:osf:metaar:9a3rw_v1 is not listed on IDEAS
    3. John M. Abowd & Ian M. Schmutte & William Sexton & Lars Vilhuber, 2019. "Suboptimal Provision of Privacy and Statistical Accuracy When They are Public Goods," Papers 1906.09353, arXiv.org.
    4. Katherine B. Coffman & Lucas C. Coffman & Keith M. Marzilli Ericson, 2017. "The Size of the LGBT Population and the Magnitude of Antigay Sentiment Are Substantially Underestimated," Management Science, INFORMS, vol. 63(10), pages 3168-3186, October.
    5. Khai Xiang Chiong & Matthew Shum, 2019. "Random Projection Estimation of Discrete-Choice Models with Large Choice Sets," Management Science, INFORMS, vol. 65(1), pages 256-271, January.
    6. Aras Selvi & Huikang Liu & Wolfram Wiesemann, 2026. "Differential Privacy via Distributionally Robust Optimization," Operations Research, INFORMS, vol. 74(1), pages 356-376, January.
    7. Tommaso Bondi & Omid Rafieian & Yunfei (Jesse) Yao, 2026. "Privacy and Polarization: An Inference-Based Framework," Management Science, INFORMS, vol. 72(2), pages 1389-1409, February.
    8. Yosuke Uno & Akira Sonoda & Masaki Bessho, 2021. "The Economics of Privacy: A Primer Especially for Policymakers," Bank of Japan Working Paper Series 21-E-11, Bank of Japan.
    9. Evan S. Totty & Thor Watson, 2026. "Privacy Protection and Accuracy: What Do We Know? Do We Know Things?? Let’s Find Out!," NBER Chapters, in: Data Privacy Protection and the Conduct of Applied Research: Methods, Approaches and New Findings, National Bureau of Economic Research, Inc.
    10. Alessandro Acquisti & Curtis Taylor & Liad Wagman, 2016. "The Economics of Privacy," Journal of Economic Literature, American Economic Association, vol. 54(2), pages 442-492, June.
    11. Inbal Dekel & Rachel Cummings & Ori Heffetz & Katrina Ligett, 2026. "Privacy Elasticity: Modeling Responses to Quantifiable Privacy Changes," NBER Chapters, in: Data Privacy Protection and the Conduct of Applied Research: Methods, Approaches and New Findings, National Bureau of Economic Research, Inc.
    12. Bharadwaj, Prashant & Pai, Mallesh M. & Suziedelyte, Agne, 2017. "Mental health stigma," Economics Letters, Elsevier, vol. 159(C), pages 57-60.
    13. Ran Eilat & Kfir Eliaz Eliaz & Xiaosheng Mu, 2021. "Bayesian Privacy," Working Papers 2021-65, Princeton University. Economics Department..
    14. Jiang, Gaoyang & Peng, Jie & Liang, Xintong & Pan, Junyu, 2025. "Supply chain digitization and continuous green innovation: Evidence from China," Energy Economics, Elsevier, vol. 142(C).
    15. John M. Abowd & Ian M. Schmutte, 2017. "Revisiting the Economics of Privacy: Population Statistics and Confidentiality Protection as Public Goods," Working Papers 17-37, Center for Economic Studies, U.S. Census Bureau.
    16. Martin Browning & Thomas F. Crossley & Joachim Winter, 2014. "The Measurement of Household Consumption Expenditures," Annual Review of Economics, Annual Reviews, vol. 6(1), pages 475-501, August.
    17. Rachel Cummings & Federico Echenique & Adam Wierman, 2016. "The Empirical Implications of Privacy-Aware Choice," Operations Research, INFORMS, vol. 64(1), pages 67-78, February.
    18. Kobbi Nissim & Rann Smorodinsky & Moshe Tennenholtz, 2018. "Segmentation, Incentives, and Privacy," Mathematics of Operations Research, INFORMS, vol. 43(4), pages 1252-1268, November.
    19. McLean, Richard P. & Postlewaite, Andrew, 2017. "A dynamic non-direct implementation mechanism for interdependent value problems," Games and Economic Behavior, Elsevier, vol. 101(C), pages 34-48.

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    More about this item

    JEL classification:

    • C49 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Other
    • C89 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other
    • D89 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Other
    • Z00 - Other Special Topics - - General - - - General

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