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Lookup NU author(s): Dr Maksim Kalameyets, Dr Shola OlabodeORCiD, Dr Vasilis VlachokyriakosORCiD, Professor Ben FarrandORCiD, Dr Lei ShiORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Current data protection legal frameworks, including the GDPR, clas- sify “special” categories of personal data that are deemed deserving of higher protection due to their impact on fundamental rights. Yet, these legal abstractions fail to capture how individuals themselves judge sensitivity in everyday digital contexts. This disconnect may undermine intelligibility and erode trust in data protection as a legal institution. Despite its centrality to privacy protection, limited empirical work has systematically compared public sensitivity judg- ments against the special categories of protected data under Article 9 GDPR. We address this gap through a mixed-methods design that integrates nine semi-structured interviews with a game-like sur- vey deployed on public arcade machines. This approach generated 2,935 responses from 224 participants enabling in-situ analysis of everyday judgments. By operationalising an ontology capturing who collects data, what data are collected, and for what purpose, we systematically compared responses across demographic groups. Contrary to literature assumptions that health and financial data are primary markers of data sensitivity, our findings demonstrate that expressive content, messages, photos, and social ties elicited the strongest resistance to sharing by citizens. Acceptance was shaped decisively by purpose. Citizens tolerated safety and functionality, whilst advertising and vague claims of “research” were rejected. Attitudes varied systematically, with women disproportionately resistant to sharing expressive content, and higher education and digital literacy predicting greater caution. This study demonstrates that data sensitivity cannot be reduced to fixed legal categories. Rather, it is socially situated and purpose-dependent. Our find- ings provide empirical foundations for reimagining consent flows, privacy defaults, and transparency mechanisms that align with everyday logics. This can enable the development of systems that people can genuinely understand, trust, and consent to.
Author(s): Kalameyets M, Owens R, Lam C, Olabode S, Vlachokyriakos V, Farrand B, Aidinlis S, Shi L
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: IUI '26: Proceedings of the 31st International Conference on Intelligent User Interfaces
Year of Conference: 2026
Pages: 1283-1297
Online publication date: 22/03/2026
Acceptance date: 12/12/2025
Date deposited: 04/02/2026
Publisher: ACM
URL: https://doi.org/10.1145/3742413.3789162
DOI: 10.1145/3742413.3789162
Data Access Statement: The arcade-machine dataset analysed in this paper is publicly available on Kaggle via the link: https://doi.org/10.34740/kaggle/dsv/14571715 A short demonstration video of the arcade machine and interaction flow is available on YouTube: https://youtube.com/shorts/-y8s17dDrdQ?feature=share
Library holdings: Search Newcastle University Library for this item
ISBN: 9798400719844
