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Releasing survey microdata with exact cluster locations and additional privacy safeguards

Author

Listed:
  • Till Koebe

    (Saarland University)

  • Alejandra Arias-Salazar

    (University of Costa Rica)

  • Timo Schmid

    (Otto Friedrich University Bamberg)

Abstract

Household survey programs around the world publish fine-granular georeferenced microdata to support research on the interdependence of human livelihoods and their surrounding environment. To safeguard the respondents’ privacy, micro-level survey data is usually (pseudo)-anonymized through deletion or perturbation procedures such as obfuscating the true location of data collection. This, however, poses a challenge to emerging approaches that augment survey data with auxiliary information on a local level. Here, we propose an alternative microdata dissemination strategy that leverages the utility of the original microdata with additional privacy safeguards through synthetically generated data using generative models. We back our proposal with experiments using data from the 2011 Costa Rican census and satellite-derived auxiliary information. Our strategy reduces the respondents’ re-identification risk for any number of disclosed attributes by 60–80% even under re-identification attempts.

Suggested Citation

  • Till Koebe & Alejandra Arias-Salazar & Timo Schmid, 2023. "Releasing survey microdata with exact cluster locations and additional privacy safeguards," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-13, December.
    • Handle: RePEc:pal:palcom:v:10:y:2023:i:1:d:10.1057_s41599-023-01694-y
    DOI: 10.1057/s41599-023-01694-y
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    1. Eszter Bokányi & Dániel Kondor & László Dobos & Tamás Sebők & József Stéger & István Csabai & Gábor Vattay, 2016. "Race, religion and the city: twitter word frequency patterns reveal dominant demographic dimensions in the United States," Palgrave Communications, Palgrave Macmillan, vol. 2(1), pages 1-9, December.
    2. Sophie Mitra & Debra L. Brucker, 2017. "Income Poverty and Multiple Deprivations in a High-Income Country: The Case of the United States," Social Science Quarterly, Southwestern Social Science Association, vol. 98(1), pages 37-56, March.
    3. Chad M. Topaz & Jude Higdon & Avriel Epps-Darling & Ethan Siau & Harper Kerkhoff & Shivani Mendiratta & Eric Young, 2022. "Race- and gender-based under-representation of creative contributors: art, fashion, film, and music," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-11, December.
    4. Douglas R. Leasure & Warren C. Jochem & Eric M. Weber & Vincent Seaman & Andrew J. Tatem, 2020. "National population mapping from sparse survey data: A hierarchical Bayesian modeling framework to account for uncertainty," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 117(39), pages 24173-24179, September.
    5. Guanghua Chi & Han Fang & Sourav Chatterjee & Joshua E. Blumenstock, 2022. "Microestimates of wealth for all low- and middle-income countries," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 119(3), pages 2113658119-, January.
    6. Emily Aiken & Suzanne Bellue & Dean Karlan & Chris Udry & Joshua E. Blumenstock, 2022. "Machine learning and phone data can improve targeting of humanitarian aid," Nature, Nature, vol. 603(7903), pages 864-870, March.
    7. repec:bla:jorssa:v:180:y:2017:i:4:p:1163-1190 is not listed on IDEAS
    8. Joshua E. Blumenstock, 2018. "Estimating Economic Characteristics with Phone Data," AEA Papers and Proceedings, American Economic Association, vol. 108, pages 72-76, May.
    9. Luc Rocher & Julien M. Hendrickx & Yves-Alexandre de Montjoye, 2019. "Estimating the success of re-identifications in incomplete datasets using generative models," Nature Communications, Nature, vol. 10(1), pages 1-9, December.
    10. Till Koebe, 2020. "Better coverage, better outcomes? Mapping mobile network data to official statistics using satellite imagery and radio propagation modelling," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-28, November.
    11. Tim Janke & Mohamed Ghanmi & Florian Steinke, 2021. "Implicit Generative Copulas," Papers 2109.14567, arXiv.org, revised Nov 2021.
    12. Byungduk Jeong & Wonjoon Lee & Deok-Soo Kim & Hayong Shin, 2016. "Copula-Based Approach to Synthetic Population Generation," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-28, August.
    13. Templ, Matthias & Meindl, Bernhard & Kowarik, Alexander & Dupriez, Olivier, 2017. "Simulation of Synthetic Complex Data: The R Package simPop," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 79(i10).
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