Abstract
The digital transformation of the Justice domain and the resulting availability of vast amounts of data describing people and their criminal behaviors offer significant promise to feed multiple research areas and enhance the criminal justice system. Achieving this vision requires the integration of different sources to create an accurate and unified representation that enables detailed and extensive data analysis. However, the collection and processing of sensitive legal-related data about individuals imposes consideration of privacy legislation and confidentiality implications. This paper presents the lesson learned from the design and develop of a Privacy-Preserving Data Integration (PPDI) architecture and process to address the challenges and opportunities of integrating personal data belonging to criminal and court sources within the Italian Justice Domain in compliance with GDPR.
This research was partly funded by the CRUI Foundation (Conferenza dei Rettori delle Universitá Italiane), within the scope of the “Recidivism Data Mart and Criminal Data Warehouse” project.
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Notes
- 1.
The detailed description of these techniques is beyond the scope of this article, please refer to [6].
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Acknowledgment
We wish to thank all the members of DBGroup. Lisa Trigiante wishes to mention that her PhD project is founded by MIUR under D.M.351 with the Emilia Romagna region as partner.
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Trigiante, L., Beneventano, D., Bergamaschi, S. (2023). Privacy-Preserving Data Integration for Digital Justice. In: Sales, T.P., Araújo, J., Borbinha, J., Guizzardi, G. (eds) Advances in Conceptual Modeling. ER 2023. Lecture Notes in Computer Science, vol 14319. Springer, Cham. https://doi.org/10.1007/978-3-031-47112-4_16
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