IDEAS home Printed from https://ideas.repec.org/p/ecb/ecbsps/201724.html
   My bibliography  Save this paper

Spontaneous recognition: an unnecessary control on data access?

Author

Listed:
  • Ritchie, Felix

Abstract

Social scientists increasingly expect to have access to detailed data for research purposes. As the level of detail increases, data providers worry about “spontaneous recognition”, the likelihood that a microdata user believes that he or she has accidentally identified one of the data subjects in the dataset, and may share that information. This concern, particularly in respect of microdata on businesses, leads to excessive restrictions on data use. We argue that spontaneous recognition presents no meaningful risk to confidentiality. The standard models of deliberate attack on the data cover re-identification risk to an acceptable standard under most current legislation. If spontaneous recognition did occur, the user is very unlikely to be in breach of any law or condition of access. Any breach would only occur as a result of further actions by the user to confirm or assert identity, and these should be seen as a managerial problem. Nevertheless, a consideration of spontaneous recognition does highlight some of the implicit assumptions made in data access decisions. It also shows the importance of the data provider’s culture and attitude. For data providers focused on users, spontaneous recognition is a useful check on whether all relevant risks have been addressed. For data providers primarily concerned with the risks of release, it provides a way to place insurmountable barriers in front of those wanting to increase data access. We present a case study on a business dataset to show how rejecting the concept of spontaneous recognition led to a substantial change in research outcomes. JEL Classification: C19, C81

Suggested Citation

  • Ritchie, Felix, 2017. "Spontaneous recognition: an unnecessary control on data access?," Statistics Paper Series 24, European Central Bank.
    • Handle: RePEc:ecb:ecbsps:201724
    as

    Download full text from publisher

    File URL: https://www.ecb.europa.eu//pub/pdf/scpsps/ecb.sps24.en.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Felix Ritchie, 2014. "Resistance to change in government: risk, inertia and incentives," Working Papers 20141412, Department of Accounting, Economics and Finance, Bristol Business School, University of the West of England, Bristol.
    2. Felix Ritchie, 2016. "Can a change in attitudes improve effective access to administrative data for research?," Working Papers 20161607, Department of Accounting, Economics and Finance, Bristol Business School, University of the West of England, Bristol.
    3. Ritchie Felix, 2014. "Access to Sensitive Data: Satisfying Objectives Rather than Constraints," Journal of Official Statistics, Sciendo, vol. 30(3), pages 1-13, September.
    4. Hans-Peter Hafner & Felix Ritchie & Rainer Lenz, 2015. "User-focused threat identification for anonymised microdata," Working Papers 20151503, Department of Accounting, Economics and Finance, Bristol Business School, University of the West of England, Bristol.
    5. Chris Skinner, 2012. "Statistical Disclosure Risk: Separating Potential and Harm," International Statistical Review, International Statistical Institute, vol. 80(3), pages 349-368, December.
    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. Tanvi Desai & Felix Ritchie & Richard Welpton, 2016. "Five Safes: designing data access for research," Working Papers 20161601, Department of Accounting, Economics and Finance, Bristol Business School, University of the West of England, Bristol.
    2. Felix Ritchie & Mark Elliot, 2015. "Principles- versus rules-based output statistical disclosure control in remote access environments," Working Papers 20151501, Department of Accounting, Economics and Finance, Bristol Business School, University of the West of England, Bristol.
    3. Felix Ritchie, 2014. "Resistance to change in government: risk, inertia and incentives," Working Papers 20141412, Department of Accounting, Economics and Finance, Bristol Business School, University of the West of England, Bristol.
    4. Torugsa, Nuttaneeya (Ann) & Arundel, Anthony, 2017. "Rethinking the effect of risk aversion on the benefits of service innovations in public administration agencies," Research Policy, Elsevier, vol. 46(5), pages 900-910.
    5. Felix Ritchie & Jim Smith, 2019. "Confidentiality and linked data," Papers 1907.06465, arXiv.org.
    6. Joseph Ficek & Wei Wang & Henian Chen & Getachew Dagne & Ellen Daley, 2021. "A Survey of Differentially Private Regression for Clinical and Epidemiological Research," International Statistical Review, International Statistical Institute, vol. 89(1), pages 132-147, April.
    7. Ian Lundberg & Arvind Narayanan & Karen Levy & Matthew Salganik, 2018. "Privacy, ethics, and data access: A case study of the Fragile Families Challenge," Working Papers wp18-09-ff, Princeton University, School of Public and International Affairs, Center for Research on Child Wellbeing..
    8. Rainer Lenz, 2016. "Recent advances in cyclic perturbation of frequency tables [Neue Entwicklungen in der zyklischen Überlagerung von Fallzahltabellen]," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 10(1), pages 37-62, February.
    9. Rainer Lenz, 2016. "Recent advances in cyclic perturbation of frequency tables," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 10(1), pages 37-62, February.
    10. Felix Ritchie & Richard Welpton, 2014. "Addressing the human factor in data access: incentive compatibility, legitimacy and cost-effectiveness in public data resources," Working Papers 20141413, Department of Accounting, Economics and Finance, Bristol Business School, University of the West of England, Bristol.

    More about this item

    Keywords

    confidentiality; data access; identification; spontaneous recognition; statistical disclosure control;
    All these keywords.

    JEL classification:

    • C19 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Other
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access

    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:ecb:ecbsps:201724. 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: Official Publications (email available below). General contact details of provider: https://edirc.repec.org/data/emieude.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.