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Five Safes: designing data access for research

Author

Listed:
  • Tanvi Desai

    (University of Essex)

  • Felix Ritchie

    (University of the West of England, Bristol)

  • Richard Welpton

    (University of the West of England, Bristol)

Abstract

What is the best way of managing access to sensitive data? This is not a straightforward question, as it involves the interaction of legal, technical, statistical and, above all, human components to produce a solution. This paper introduces a modelling tool designed to simplify and structure such decision-making. The Five Safes model is a popular framework for designing, describing and evaluating access systems for data, used by data providers, data users, and regulators. The model integrates analysis of opportunities, constraints, costs and benefits of different approaches, taking account of the level of data anonymisation, the likely users, the scope for training, the environment through which data are accessed, and the statistical outputs derived from data use. Up to now this model has largely been described indirectly in other papers which have used it as a framing device. This paper focuses specifically on the framework, discusses usage, and demonstrates where it sits with other data and risk management tools. The aim is to provide a practical guide to the effective planning and management of access to research data.

Suggested Citation

  • 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.
    • Handle: RePEc:uwe:wpaper:20161601
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    File URL: http://www2.uwe.ac.uk/faculties/BBS/Documents/1601.pdf
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    References listed on IDEAS

    as
    1. 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.
    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. Ritchie Felix, 2014. "Access to Sensitive Data: Satisfying Objectives Rather than Constraints," Journal of Official Statistics, Sciendo, vol. 30(3), pages 533-545, September.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Peng Zhang & Maged N. Kamel Boulos, 2022. "Privacy-by-Design Environments for Large-Scale Health Research and Federated Learning from Data," IJERPH, MDPI, vol. 19(19), pages 1-13, September.
    2. David H. Schiller & Johanna Eberle & Daniel Fuß & Jan Goebel & Jörg Heining & Tatjana Mika & Dana Müller & Frank Röder & Michael Stegmann & Karsten Stephan, 2017. "Standards des sicheren Datenzugangs in den Sozial- und Wirtschaftswissenschaften - Überblick über verschiedene Remote-Access-Verfahren," RatSWD Working Papers 261, German Data Forum (RatSWD).
    3. Sallie-Anne Pearson & Nicole Pratt & Juliana de Oliveira Costa & Helga Zoega & Tracey-Lea Laba & Christopher Etherton-Beer & Frank M. Sanfilippo & Alice Morgan & Lisa Kalisch Ellett & Claudia Bruno & , 2021. "Generating Real-World Evidence on the Quality Use, Benefits and Safety of Medicines in Australia: History, Challenges and a Roadmap for the Future," IJERPH, MDPI, vol. 18(24), pages 1-20, December.
    4. Kalinda E. Griffiths & Jessica Blain & Claire M. Vajdic & Louisa Jorm, 2021. "Indigenous and Tribal Peoples Data Governance in Health Research: A Systematic Review," IJERPH, MDPI, vol. 18(19), pages 1-22, September.
    5. Boylan, Sally & Arsenault, Catherine & Barreto, Marcos & Bozza, Fernando A & Fonseca, Adalton & Forde, Eoghan & Hookham, Lauren & Humphreys, Georgina S & Ichihara, Maria Yury & Le doare, Kirsty & Liu,, 2024. "Data challenges for international health emergencies: lessons learned from ten international COVID-19 driver projects," LSE Research Online Documents on Economics 122811, London School of Economics and Political Science, LSE Library.
    6. Ian Foster, 2018. "Research Infrastructure for the Safe Analysis of Sensitive Data," The ANNALS of the American Academy of Political and Social Science, , vol. 675(1), pages 102-120, January.

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    More about this item

    Keywords

    data access; data management; confidentiality; security engineering; statistical disclosure control;
    All these keywords.

    JEL classification:

    • 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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