Pseudonymization risk analysis in distributed systems

Geoffrey Neumann, Paul Grace, Daniel Burns, Michael Surridge

    Research output: Contribution to journalArticlepeer-review


    In an era of big data, online services are becoming increasingly data-centric; they collect, process, analyze and anonymously disclose growing amounts of personal data in the form of pseudonymized data sets. It is crucial that such systems are engineered to both protect individual user (data subject) privacy and give back control of personal data to the user. In terms of pseudonymized data this means that unwanted individuals should not be able to deduce sensitive information about the user. However, the plethora of pseudonymization algorithms and tuneable parameters that currently exist make it difficult for a non expert developer (data controller) to understand and realise strong privacy guarantees. In this paper we propose a principled Model-Driven Engineering (MDE) framework to model data services in terms of their pseudonymization strategies and identify the risks to breaches of user privacy. A developer can explore alternative pseudonymization strategies to determine the effectiveness of their pseudonymization strategy in terms of quantifiable metrics: i) violations of privacy requirements for every user in the current data set; ii) the trade-off between conforming to these requirements and the usefulness of the data for its intended purposes. We demonstrate through an experimental evaluation that the information provided by the framework is useful, particularly in complex situations where privacy requirements are different for different users, and can inform decisions to optimize a chosen strategy in comparison to applying an off-the-shelf algorithm.
    Original languageEnglish
    Article number1
    JournalJournal of Internet Services and Applications
    Issue number1
    Publication statusPublished - 8 Jan 2019

    Bibliographical note

    © The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
    International License (, which permits unrestricted use, distribution, and
    reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the
    Creative Commons license, and indicate if changes were made


    • Privacy
    • Pseudonymization
    • Risk analysis


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