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Privacy-Preserving Data Integration for Digital Justice

Contributo in Atti di convegno
Data di Pubblicazione:
2023
Citazione:
Privacy-Preserving Data Integration for Digital Justice / Trigiante, L.; Beneventano, D.; Bergamaschi, S.. - 14319:(2023), pp. 172-177. ( 42nd International Conference on Conceptual Modeling, ER 2023 Lisbon, PORTUGAL NOV 06-09, 2023) [10.1007/978-3-031-47112-4_16].
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.
Tipologia CRIS:
Relazione in Atti di Convegno
Keywords:
Digitization; Legal Data; PPDI; PPRL; Pseudonym
Elenco autori:
Trigiante, L.; Beneventano, D.; Bergamaschi, S.
Autori di Ateneo:
BENEVENTANO Domenico
BERGAMASCHI Sonia
Link alla scheda completa:
https://iris.unimore.it/handle/11380/1329550
Titolo del libro:
ADVANCES IN CONCEPTUAL MODELING, ER 2023 WORKSHOPS
Pubblicato in:
LECTURE NOTES IN COMPUTER SCIENCE
Journal
LECTURE NOTES IN COMPUTER SCIENCE
Series
  • Dati Generali

Dati Generali

URL

https://link.springer.com/chapter/10.1007/978-3-031-47112-4_16
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