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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, 2014. "Privacy and Data-Based Research," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 75-98, Spring.
  • Handle: RePEc:aea:jecper:v:28:y:2014:i:2:p:75-98
    Note: DOI: 10.1257/jep.28.2.75
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    File URL: http://www.aeaweb.org/articles.php?doi=10.1257/jep.28.2.75
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    References listed on IDEAS

    as
    1. Peter L. Rousseau, 2022. "Report of the Secretary," AEA Papers and Proceedings, American Economic Association, vol. 112, pages 646-649, May.
    2. Peter L. Rousseau, 2022. "Report of the Secretary," AEA Papers and Proceedings, American Economic Association, vol. 112, pages 646-649, May.
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    4. Peter L. Rousseau, 2022. "Report of the Secretary," AEA Papers and Proceedings, American Economic Association, vol. 112, pages 646-649, May.
    5. Peter L. Rousseau, 2022. "Report of the Secretary," AEA Papers and Proceedings, American Economic Association, vol. 112, pages 646-649, May.
    6. 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.
    7. Peter L. Rousseau, 2022. "Report of the Secretary," AEA Papers and Proceedings, American Economic Association, vol. 112, pages 646-649, May.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. 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.
    2. Christensen, Garret & Miguel, Edward & Sturdy, Jennifer, 2017. "Transparency, Reproducibility, and the Credibility of Economics Research," MetaArXiv 9a3rw, Center for Open Science.
    3. 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.
    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. 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.
    6. 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.
    7. Bharadwaj, Prashant & Pai, Mallesh M. & Suziedelyte, Agne, 2017. "Mental health stigma," Economics Letters, Elsevier, vol. 159(C), pages 57-60.
    8. 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.
    9. Ran Eilat & Kfir Eliaz Eliaz & Xiaosheng Mu, 2021. "Bayesian Privacy," Working Papers 2021-65, Princeton University. Economics Department..
    10. 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.
    11. Rachel Cummings & Federico Echenique & Adam Wierman, 2016. "The Empirical Implications of Privacy-Aware Choice," Operations Research, INFORMS, vol. 64(1), pages 67-78, February.
    12. 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.
    13. Kobbi Nissim & Rann Smorodinsky & Moshe Tennenholtz, 2018. "Segmentation, Incentives, and Privacy," Mathematics of Operations Research, INFORMS, vol. 43(4), pages 1252-1268, November.
    14. 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.

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    More about this item

    JEL classification:

    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

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