IDEAS home Printed from https://ideas.repec.org/a/kap/poprpr/v42y2023i5d10.1007_s11113-023-09829-4.html
   My bibliography  Save this article

Assessing the Impact of Differential Privacy on Population Uniques in Geographically Aggregated Data: The Case of the 2020 U.S. Census

Author

Listed:
  • Yue Lin

    (The Ohio State University
    University of Chicago)

  • Ningchuan Xiao

    (The Ohio State University)

Abstract

Geographically aggregated demographic, social, and economic data are valuable for research and practical applications, but their use and sharing often compromise individual privacy. The U.S. Census Bureau has responded to this issue by introducing a new privacy protection method, the TopDown Algorithm (TDA), in the 2020 Census. The TDA is based on a privacy definition known as differential privacy and is primarily designed to reduce the risk of reconstruction-abetted disclosure, a type of privacy violation where individual identities can be revealed by reconstructing confidential census responses and linking them to publicly available data. However, there still lacks a systematic exploration of the impact of the TDA on direct disclosure, another common type of privacy violation where individuals can be directly distinguished from public census tables to reveal their identities. To address this gap, this paper examines the effectiveness of the TDA in protecting against direct disclosure by focusing on how information from public census tables can be used to distinguish population uniques, the individuals that can be uniquely distinguished from census tables. Our study reveals that while the TDA provides a reasonable level of differential privacy, it does not necessarily prevent the direct identification of population uniques using public census tables. Our finding is crucial for policymakers to consider when making informed decisions regarding parameter selection for the TDA during its implementation.

Suggested Citation

  • Yue Lin & Ningchuan Xiao, 2023. "Assessing the Impact of Differential Privacy on Population Uniques in Geographically Aggregated Data: The Case of the 2020 U.S. Census," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 42(5), pages 1-20, October.
    • Handle: RePEc:kap:poprpr:v:42:y:2023:i:5:d:10.1007_s11113-023-09829-4
    DOI: 10.1007/s11113-023-09829-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11113-023-09829-4
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11113-023-09829-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Louise T. Su, 1994. "The relevance of recall and precision in user evaluation," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 45(3), pages 207-217, April.
    2. Yue Lin & Ningchuan Xiao, 2023. "A Computational Framework for Preserving Privacy and Maintaining Utility of Geographically Aggregated Data: A Stochastic Spatial Optimization Approach," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 113(5), pages 1035-1056, May.
    3. Skinner, Chris & Shlomo, Natalie, 2008. "Assessing Identification Risk in Survey Microdata Using Log-Linear Models," Journal of the American Statistical Association, American Statistical Association, vol. 103(483), pages 989-1001.
    4. John M. Abowd & Robert Ashmead & Ryan Cumings-Menon & Simson Garfinkel & Micah Heineck & Christine Heiss & Robert Johns & Daniel Kifer & Philip Leclerc & Ashwin Machanavajjhala & Brett Moran & William, 2022. "The 2020 Census Disclosure Avoidance System TopDown Algorithm," Papers 2204.08986, arXiv.org.
    Full references (including those not matched with items on IDEAS)

    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. Drechsler, Jörg & Reiter, Jerome P., 2011. "An empirical evaluation of easily implemented, nonparametric methods for generating synthetic datasets," Computational Statistics & Data Analysis, Elsevier, vol. 55(12), pages 3232-3243, December.
    2. Eurosystem Household Finance and Consumption Network, 2013. "The Eurosystem Household Finance and Consumption Survey - Methodological report," Statistics Paper Series 1, European Central Bank.
    3. Li‐Chun Zhang & Gustav Haraldsen, 2022. "Secure big data collection and processing: Framework, means and opportunities," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 1541-1559, October.
    4. James Jackson & Robin Mitra & Brian Francis & Iain Dove, 2022. "Using saturated count models for user‐friendly synthesis of large confidential administrative databases," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 1613-1643, October.
    5. Sergio I. Prada & Claudia González-Martínez & Joshua Borton & Johannes Fernandes-Huessy & Craig Holden & Elizabeth Hair & and Tim Mulcahy, 2011. "Avoiding Disclosure of Individually Identifiable Health Information," SAGE Open, , vol. 1(3), pages 21582440114, October.
    6. John M. Abowd & Ian M. Schmutte, 2015. "Economic Analysis and Statistical Disclosure Limitation," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 50(1 (Spring), pages 221-293.
    7. Christine M. O'Keefe & James O. Chipperfield, 2013. "A Summary of Attack Methods and Confidentiality Protection Measures for Fully Automated Remote Analysis Systems," International Statistical Review, International Statistical Institute, vol. 81(3), pages 426-455, December.
    8. Krenzke Tom & Gentleman Jane F. & Li Jianzhu & Moriarity Chris, 2013. "Addressing Disclosure Concerns and Analysis Demands in a Real-Time Online Analytic System," Journal of Official Statistics, Sciendo, vol. 29(1), pages 99-124, March.
    9. Iwona Bąk & Katarzyna Cheba, 2022. "Green Transformation: Applying Statistical Data Analysis to a Systematic Literature Review," Energies, MDPI, vol. 16(1), pages 1-22, December.
    10. Azzah Al‐Maskari & Mark Sanderson, 2010. "A review of factors influencing user satisfaction in information retrieval," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 61(5), pages 859-868, May.
    11. Prada, Sergio I & Gonzalez, Claudia & Borton, Joshua & Fernandes-Huessy, Johannes & Holden, Craig & Hair, Elizabeth & Mulcahy, Tim, 2011. "Avoiding disclosure of individually identifiable health information: a literature review," MPRA Paper 35463, University Library of Munich, Germany.
    12. Cinzia Carota & Maurizio Filippone & Silvia Polettini, 2022. "Assessing Bayesian Semi‐Parametric Log‐Linear Models: An Application to Disclosure Risk Estimation," International Statistical Review, International Statistical Institute, vol. 90(1), pages 165-183, April.
    13. Favaro, Stefano & Panero, Francesca & Rigon, Tommaso, 2021. "Bayesian nonparametric disclosure risk assessment," LSE Research Online Documents on Economics 117305, London School of Economics and Political Science, LSE Library.
    14. John M. Abowd & Ian M. Schmutte, 2015. "Economic Analysis and Statistical Disclosure Limitation," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 46(1 (Spring), pages 221-293.
    15. Chipperfield James O., 2014. "Disclosure-Protected Inference with Linked Microdata Using a Remote Analysis Server," Journal of Official Statistics, Sciendo, vol. 30(1), pages 123-146, March.
    16. Shlomo, Natalie & Skinner, Chris, 2022. "Measuring risk of re-identification in microdata: state-of-the art and new directions," LSE Research Online Documents on Economics 117168, London School of Economics and Political Science, LSE Library.

    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:kap:poprpr:v:42:y:2023:i:5:d:10.1007_s11113-023-09829-4. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.