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Spontaneous recognition: an unnecessary control on data access?

Author

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  • 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
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    File URL: https://www.ecb.europa.eu//pub/pdf/scpsps/ecb.sps24.en.pdf
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    References listed on IDEAS

    as
    1. Ritchie Felix, 2014. "Access to Sensitive Data: Satisfying Objectives Rather than Constraints," Journal of Official Statistics, Sciendo, vol. 30(3), pages 533-545, September.
    2. Chris Skinner, 2012. "Statistical Disclosure Risk: Separating Potential and Harm," International Statistical Review, International Statistical Institute, vol. 80(3), pages 349-368, December.
    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. 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.
    5. 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.
    Full references (including those not matched with items on IDEAS)

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    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

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