IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v16y2025i1d10.1038_s41467-024-55296-6.html
   My bibliography  Save this article

A scaling law to model the effectiveness of identification techniques

Author

Listed:
  • Luc Rocher

    (University of Oxford
    Université catholique de Louvain
    Imperial College London)

  • Julien M. Hendrickx

    (Université catholique de Louvain)

  • Yves-Alexandre de Montjoye

    (Imperial College London
    Imperial College London)

Abstract

AI techniques are increasingly being used to identify individuals both offline and online. However, quantifying their effectiveness at scale and, by extension, the risks they pose remains a significant challenge. Here, we propose a two-parameter Bayesian model for exact matching techniques and derive an analytical expression for correctness (κ), the fraction of people accurately identified in a population. We then generalize the model to forecast how κ scales from small-scale experiments to the real world, for exact, sparse, and machine learning-based robust identification techniques. Despite having only two degrees of freedom, our method closely fits 476 correctness curves and strongly outperforms curve-fitting methods and entropy-based rules of thumb. Our work provides a principled framework for forecasting the privacy risks posed by identification techniques, while also supporting independent accountability efforts for AI-based biometric systems.

Suggested Citation

  • Luc Rocher & Julien M. Hendrickx & Yves-Alexandre de Montjoye, 2025. "A scaling law to model the effectiveness of identification techniques," Nature Communications, Nature, vol. 16(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-024-55296-6
    DOI: 10.1038/s41467-024-55296-6
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-024-55296-6
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-024-55296-6?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
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Hoshino, Nobuaki & Akimichi Takemura, 1998. ""On the Relation between Logarithmic Series Model and Other Superpopulation Models Useful for Microdata Disclosure Risk Assessment"," CIRJE F-Series 98-F-7, CIRJE, Faculty of Economics, University of Tokyo.
    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. Nobuaki Hoshino, 2005. "On a limiting quasi-multinomial distribution," CIRJE F-Series CIRJE-F-361, CIRJE, Faculty of Economics, University of Tokyo.
    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. 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.
    15. 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.
    16. 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.
    17. 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.

    More about this item

    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:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-024-55296-6. 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.nature.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.