The CIRSFID (University of Bologna, IT), in collaboration with the Academy of Fine Arts of Bologna and Società Italiana Informatica Giuridica (SIIG), has developed DaPIS, a Data Protection Icon Set representing: (1) data processing operations and processed data (e.g., anonymized data, encrypted data), (2) purposes of the processing (e.g., marketing purposes, scientific purposes), (3) legal bases for the processing (e.g., legal obligation, consent), (4) agents and roles (e.g., data subject, data controller, supervisory authority), (5) rights of data subjects (e.g., right to access, right to erasure, right to data portability).
Selection of concepts
DaPIS’ concepts have been selected following Artt. 13-14 GDPR and have been integrated with additional important concepts, formalized in the computational ontology PrOnto. The ontological foundation was instrumental for the creation of a machine-readable icon set (as provided by the GDPR), namely an iconic language whose elements have computer-interpretable meanings that are explicitly and formally defined in the ontology. With the provision of XML mark-up to the linguistic expressions in documents, applications can semi-automatically retrieve and display the icons encoded in the ontology next to the relevant chunks of text in the document.
Most relevant publications:
- Rossi, A., and Palmirani, M., (2020). What’s in an Icon? Promises and Pitfalls of Data Protection Iconography. In: R. Leenes, D. Hallinan, S. Gutwirth and P. De Hert (Eds.), Data Protection and Privacy: Data Protection and Democracy. Oxford, Hart Publishing. (ask firstname.lastname@example.org for a copy)
- Rossi, A., and Palmirani, M., (2019). DaPIS: an Ontology-Based Data Protection Icon Set. In G. Peruginelli & S. Faro (Eds.): Knowledge of the Law in the Big Data Age. Frontiers in Artificial Intelligence and Applications. Vol. 317. IOS Press. ISBN: 978-1-61499-984-3 (print) | 978-1-61499-985-0 (online).
- Rossi, A. and Palmirani, M., From Words to Pictures Through Legal Visualizations (2018). In Ugo Pagallo et al. (Eds.): AI Approaches to the Complexity of Legal Systems: AICOL International Workshops 2015–2017: AICOL-VI@ JURIX 2015, AICOL-VII@ EKAW 2016, AICOL-VIII@ JURIX 2016, AICOL-IX@ ICAIL 2017, and AICOL-X@ JURIX 2017, Revised Selected Papers. Springer, 2018, pp. 72-85. DOI: https://doi.org/10.1007/978-3-030-00178-0_5.
- Palmirani, M., Rossi, A., Martoni, M. and Hagan, M., A Methodological Framework to Design a Machine-Readable Privacy Icon Set (2018). In Erich Schweighofer et al. (Eds.), Data Protection / LegalTech. Proceedings of the 21th International Legal Informatics Symposium IRIS 2018. Editions Weblaw, Bern 2018, pp. 451–454 (ISBN 978-3-906940-21-2) and in Jusletter IT, 22 February 2018.
- Rossi, A. and Palmirani, M., A Visualization Approach for Adaptive Consent in the European Data Protection Framework(2017). In: 2017 International Conference for E-Democracy and Open Government (CeDEM), Krems, Austria, 2017, pp. 159-170. DOI: 10.1109/CeDEM.2017.23
Further project description and relevant publications are available here