The digital transformation of different processes and the resulting availability of vast amounts of data describing people and their behaviors offer significant promise to advance multiple research areas and enhance both the public and private sectors. Exploiting the full potential of this vision requires a unified representation of different autonomous data sources to fa- cilitate detailed data analysis capacity. Collecting and processing sensitive data about individuals leads to consideration of privacy requirements and confidentiality concerns. This vision paper pro- vides a concise overview of the research field concerning Privacy- Preserving Data Integration (PPDI), the associated challenges, opportunities, and unexplored aspects, with the primary aim of designing a novel and comprehensive PPDI framework based on a Trusted Third-Party microservices architecture.

[Vision Paper] Privacy-Preserving Data Integration / Trigiante, Lisa; Beneventano, Domenico; Bergamaschi, Sonia. - (2023), pp. 5614-5618. (Intervento presentato al convegno 2023 IEEE International Conference on Big Data, BigData 2023 tenutosi a Sorrento, IT nel 18/12/2023) [10.1109/BigData59044.2023.10386703].

[Vision Paper] Privacy-Preserving Data Integration

Lisa Trigiante
;
Domenico Beneventano;Sonia Bergamaschi
2023

Abstract

The digital transformation of different processes and the resulting availability of vast amounts of data describing people and their behaviors offer significant promise to advance multiple research areas and enhance both the public and private sectors. Exploiting the full potential of this vision requires a unified representation of different autonomous data sources to fa- cilitate detailed data analysis capacity. Collecting and processing sensitive data about individuals leads to consideration of privacy requirements and confidentiality concerns. This vision paper pro- vides a concise overview of the research field concerning Privacy- Preserving Data Integration (PPDI), the associated challenges, opportunities, and unexplored aspects, with the primary aim of designing a novel and comprehensive PPDI framework based on a Trusted Third-Party microservices architecture.
2023
2023 IEEE International Conference on Big Data, BigData 2023
Sorrento, IT
18/12/2023
5614
5618
Trigiante, Lisa; Beneventano, Domenico; Bergamaschi, Sonia
[Vision Paper] Privacy-Preserving Data Integration / Trigiante, Lisa; Beneventano, Domenico; Bergamaschi, Sonia. - (2023), pp. 5614-5618. (Intervento presentato al convegno 2023 IEEE International Conference on Big Data, BigData 2023 tenutosi a Sorrento, IT nel 18/12/2023) [10.1109/BigData59044.2023.10386703].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1329552
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