[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/p/ehl/lserod/85923.html
   My bibliography  Save this paper

K-anonymity: a note on the trade-off between data utility and data security

Author

Listed:
  • Komarova, Tatiana
  • Nekipelov, Denis
  • Al Rafi, Ahnaf
  • Yakovlev, Evgeny
Abstract
Researchers often use data from multiple datasets to conduct credible econometric and statistical analysis. The most reliable way to link entries across such datasets is to exploit unique identifiers if those are available. Such linkage however may result in privacy violations revealing sensitive information about some individuals in a sample. Thus, a data curator with concerns for individual privacy may choose to remove certain individual information from the private dataset they plan on releasing to researchers. The extent of individual information the data curator keeps in the private dataset can still allow a researcher to link the datasets, most likely with some errors, and usually results in a researcher having several feasible combined datasets. One conceptual framework a data curator may rely on is k-anonymity, k ³ 2 , which gained wide popularity in computer science and statistical community. To ensure k-anonymity, the data curator releases only the amount of identifying information in the private dataset that guarantees that every entry in it can be linked to at least k different entries in the publicly available datasets the researcher will use. In this paper, we look at the data combination task and the estimation task from both perspectives – from the perspective of the researcher estimating the model and from the perspective of a data curator who restricts identifying information in the private dataset to make sure that k-anonymity holds. We illustrate how to construct identifiers in practice and use them to combine some entries across two datasets. We also provide an empirical illustration on how a data curator can ensure k-anonymity and consequences it has on the estimation procedure. Naturally, the utility of the combined data gets smaller as k increases, which is also evident from our empirical illustration

Suggested Citation

  • Komarova, Tatiana & Nekipelov, Denis & Al Rafi, Ahnaf & Yakovlev, Evgeny, 2017. "K-anonymity: a note on the trade-off between data utility and data security," LSE Research Online Documents on Economics 85923, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:85923
    as

    Download full text from publisher

    File URL: http://eprints.lse.ac.uk/85923/
    File Function: Open access version.
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Goldfarb, Avi & Greenstein, Shane M. & Tucker, Catherine E. (ed.), 2015. "Economic Analysis of the Digital Economy," National Bureau of Economic Research Books, University of Chicago Press, number 9780226206981, August.
    2. Tatiana Komarova & Denis Nekipelov & Evgeny Yakovlev, 2018. "Identification, data combination, and the risk of disclosure," Quantitative Economics, Econometric Society, vol. 9(1), pages 395-440, March.
    3. Tatiana Komarova & Denis Nekipelov & Evgeny Yakovlev, 2015. "Estimation of Treatment Effects from Combined Data: Identification versus Data Security," NBER Chapters, in: Economic Analysis of the Digital Economy, pages 279-308, National Bureau of Economic Research, Inc.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Tatiana Komarova & Denis Nekipelov, 2020. "Identification and Formal Privacy Guarantees," Papers 2006.14732, arXiv.org, revised May 2021.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tatiana Komarova & Denis Nekipelov, 2020. "Identification and Formal Privacy Guarantees," Papers 2006.14732, arXiv.org, revised May 2021.
    2. Tatiana Komarova & Denis Nekipelov & Evgeny Yakovlev, 2018. "Identification, data combination, and the risk of disclosure," Quantitative Economics, Econometric Society, vol. 9(1), pages 395-440, March.
    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. Antonelli, Cristiano, 2017. "Digital knowledge generation and the appropriability trade-off," Telecommunications Policy, Elsevier, vol. 41(10), pages 991-1002.
    5. 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.
    6. Kekezi, Orsa & Mellander, Charlotta, 2017. "Geography and Media – Does a Local Editorial Office Increase the Consumption of Local News?," Working Paper Series in Economics and Institutions of Innovation 447, Royal Institute of Technology, CESIS - Centre of Excellence for Science and Innovation Studies.
    7. Amalia R. Miller & Catherine Tucker, 2017. "Frontiers of Health Policy: Digital Data and Personalized Medicine," Innovation Policy and the Economy, University of Chicago Press, vol. 17(1), pages 49-75.
    8. Edward L. Glaeser & Hyunjin Kim & Michael Luca, 2019. "Nowcasting the Local Economy: Using Yelp Data to Measure Economic Activity," NBER Chapters, in: Big Data for Twenty-First-Century Economic Statistics, pages 249-273, National Bureau of Economic Research, Inc.
    9. Babur De los Santos & Matthijs R. Wildenbeest, 2017. "E-book pricing and vertical restraints," Quantitative Marketing and Economics (QME), Springer, vol. 15(2), pages 85-122, June.
    10. Laurent Ferrara & Anna Simoni, 2023. "When are Google Data Useful to Nowcast GDP? An Approach via Preselection and Shrinkage," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(4), pages 1188-1202, October.
    11. Sutirtha Bagchi, 2018. "A Tale of Two Cities: An Examination of Medallion Prices in New York and Chicago," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 53(2), pages 295-319, September.
    12. Rongrong Zhou & Decai Tang & Dan Da & Wenya Chen & Lin Kong & Valentina Boamah, 2022. "Research on China’s Manufacturing Industry Moving towards the Middle and High-End of the GVC Driven by Digital Economy," Sustainability, MDPI, vol. 14(13), pages 1-30, June.
    13. Steven Berry & Martin Gaynor & Fiona Scott Morton, 2019. "Do Increasing Markups Matter? Lessons from Empirical Industrial Organization," Journal of Economic Perspectives, American Economic Association, vol. 33(3), pages 44-68, Summer.
    14. Adena, Maja & Hager, Anselm, 2020. "Does online fundraising increase charitable giving? A nation-wide field experiment on Facebook," Discussion Papers, Research Unit: Economics of Change SP II 2020-302, WZB Berlin Social Science Center.
    15. Masha Krupenkin & David Rothschild & Shawndra Hill & Elad Yom-Tov, 2019. "President Trump Stress Disorder: Partisanship, Ethnicity, and Expressive Reporting of Mental Distress After the 2016 Election," SAGE Open, , vol. 9(1), pages 21582440198, March.
    16. Antonelli, Cristiano & Tubiana, Matteo, 2020. "Income inequality in the knowledge economy," Structural Change and Economic Dynamics, Elsevier, vol. 55(C), pages 153-164.
    17. Nivín, Rafael & Pérez, Fernando, 2019. "Estimación de un Índice de Condiciones Financieras para el Perú," Revista Estudios Económicos, Banco Central de Reserva del Perú, issue 37, pages 49-64.
    18. Joel Waldfogel, 2017. "How Digitization Has Created a Golden Age of Music, Movies, Books, and Television," Journal of Economic Perspectives, American Economic Association, vol. 31(3), pages 195-214, Summer.
    19. Azzellini, Dario & Greer, Ian & Umney, Charles, 2019. "Limits of the platform economy: Digitalization and marketization in live music," Working Paper Forschungsförderung 154, Hans-Böckler-Stiftung, Düsseldorf.
    20. Boğa Semra & Topcu Murat, 2020. "Creative Economy: A Literature Review on Relational Dimensions, Challanges, and Policy Implications," Economics, Sciendo, vol. 8(2), pages 149-169, December.

    More about this item

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ehl:lserod:85923. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: LSERO Manager (email available below). General contact details of provider: https://edirc.repec.org/data/lsepsuk.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.